{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Statistics for Data Scientists (Python)\n",
    "# Chapter 4. Regression and Prediction\n",
    "> (c) 2019 Peter C. Bruce, Andrew Bruce, Peter Gedeck"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import required Python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:03.864611Z",
     "iopub.status.busy": "2022-04-26T19:56:03.863919Z",
     "iopub.status.idle": "2022-04-26T19:56:06.261770Z",
     "shell.execute_reply": "2022-04-26T19:56:06.260726Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no display found. Using non-interactive Agg backend\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.stats.outliers_influence import OLSInfluence\n",
    "\n",
    "from pygam import LinearGAM, s, l\n",
    "from pygam.datasets import wage\n",
    "\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from dmba import stepwise_selection\n",
    "from dmba import AIC_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:06.282478Z",
     "iopub.status.busy": "2022-04-26T19:56:06.282202Z",
     "iopub.status.idle": "2022-04-26T19:56:06.287171Z",
     "shell.execute_reply": "2022-04-26T19:56:06.286442Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:06.289924Z",
     "iopub.status.busy": "2022-04-26T19:56:06.289724Z",
     "iopub.status.idle": "2022-04-26T19:56:07.087244Z",
     "shell.execute_reply": "2022-04-26T19:56:07.086047Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    import common\n",
    "    DATA = common.dataDirectory()\n",
    "except ImportError:\n",
    "    DATA = Path().resolve() / 'data'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define paths to data sets. If you don't keep your data in the same directory as the code, adapt the path names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:07.090267Z",
     "iopub.status.busy": "2022-04-26T19:56:07.090082Z",
     "iopub.status.idle": "2022-04-26T19:56:07.093554Z",
     "shell.execute_reply": "2022-04-26T19:56:07.092838Z"
    }
   },
   "outputs": [],
   "source": [
    "LUNG_CSV = DATA / 'LungDisease.csv'\n",
    "HOUSE_CSV = DATA / 'house_sales.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Linear Regression\n",
    "## The Regression Equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:07.096006Z",
     "iopub.status.busy": "2022-04-26T19:56:07.095838Z",
     "iopub.status.idle": "2022-04-26T19:56:07.230644Z",
     "shell.execute_reply": "2022-04-26T19:56:07.229334Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lung = pd.read_csv(LUNG_CSV)\n",
    "\n",
    "lung.plot.scatter(x='Exposure', y='PEFR')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `LinearRegression` model from _scikit-learn_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:07.234507Z",
     "iopub.status.busy": "2022-04-26T19:56:07.233589Z",
     "iopub.status.idle": "2022-04-26T19:56:07.242651Z",
     "shell.execute_reply": "2022-04-26T19:56:07.241777Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: 424.583\n",
      "Coefficient Exposure: -4.185\n"
     ]
    }
   ],
   "source": [
    "predictors = ['Exposure']\n",
    "outcome = 'PEFR'\n",
    "\n",
    "model = LinearRegression()\n",
    "model.fit(lung[predictors], lung[outcome])\n",
    "\n",
    "print(f'Intercept: {model.intercept_:.3f}')\n",
    "print(f'Coefficient Exposure: {model.coef_[0]:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:07.245654Z",
     "iopub.status.busy": "2022-04-26T19:56:07.245475Z",
     "iopub.status.idle": "2022-04-26T19:56:07.578355Z",
     "shell.execute_reply": "2022-04-26T19:56:07.577348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LIzdTMwlZfZhXvorherKMNy2EeDAJhnKuZe0qbBn/DOO7NESv1bDj5HX850TyzYE4OXoQQtyXBEMFYKPXERrwBJvHP0Mzj8qkZOQQtt7IC5/v57KMNy2E+B8SDBVIIzcD60e34+3AxthZ6dh/MZFu8/awNFLGmxZC/JcEQwWj02r4V/t67JjYgWca5I83PWPbaZ5btI8T8TLetBBCgqHC8nC256vhrZn1D18MtnqMV5Po88mvzJLxpoWo8CQYKjCNRsOAVh789FpHevq4kZunsGj3BXrO38uBS4lqlyeEUIkEg8Clki2LBrVk6ZCWuFSy4eLNNAYsjeLtDUZSZLxpISocCQZh1q2JGxGhHXn+KQ8Avt4fR9e5e9h5SsabFqIikWAQFpzsrJjZ35fVI9pQu6o915IyGL7yEOO/OcItGW9aiApBgkHcV7v61fjx1Q680qEeWg1sPBqP/5xIwo/IeNNClHelJhhmzpyJRqNhwoQJ5nkZGRmEhIRQtWpVHB0d6d+/P9evW57WiIuLIzAwEHt7e1xcXHj99dfJyZHupouCnbWOsJ6N2RDyNI3cKvHHnWwmfnuUYStkvGkhyrNSEQwHDx5k6dKl+Pr6WsyfOHEimzZtYu3atURGRhIfH0+/fv3My3NzcwkMDCQrK4t9+/axcuVKVqxYwbvvvlvSu1Cu+daqzKZxz/B6t/zxpnefyR9veuW+y+TJeNNClD+KylJSUpSGDRsqERERSseOHZVXX31VURRFuX37tmJlZaWsXbvW3PbUqVMKoERFRSmKoihbt25VtFqtYjKZzG0WL16sGAwGJTMzs8A1JCUlKYCSlJRUNDtVjp1PSFH+sfhXpfakzUrtSZuVfot+Vc5dT1a7LCHEQxTmd071I4aQkBACAwPx9/e3mB8TE0N2drbF/EaNGuHp6UlUVBQAUVFR+Pj44Orqam7TrVs3kpOTOXHixAO3mZmZSXJyssUkCqZ+dUe+HenHe0FNcLDWEfPbH/Sc/wsLdsp400KUF6oGw5o1azh8+DAzZsy4Z5nJZMLa2prKlStbzHd1dcVkMpnb/DUU7i6/u+xBZsyYgZOTk3ny8PB4zD2pWLRaDUP86hAR2pHOjVzIys1jTsRZ+nzyC0ev3Fa7PCHEY1ItGK5cucKrr77KqlWrsLW1LdFth4WFkZSUZJ6uXLlSotsvL9wr2/F/wa2Y/3xznB2sOW1K4blFv/L+ZhlvWoiyTLVgiImJISEhgRYtWqDX69Hr9URGRrJgwQL0ej2urq5kZWVx+/Zti/ddv34dNzc3ANzc3O65S+nu33fb3I+NjQ0Gg8FiEo9Go9EQ1LwmP4V2pG9zd/IUWPbLJbrN28Ov52W8aSHKItWCoUuXLhiNRmJjY81Tq1atGDRokPm1lZUVO3fuNL/nzJkzxMXF4efnB4Cfnx9Go5GEhARzm4iICAwGA97e3iW+TxWZs4M1855/kuXDnsLdyZYriekMWhbNf74/StId6VZDiLJEtWCoVKkSTZs2tZgcHByoWrUqTZs2xcnJieHDhxMaGsrPP/9MTEwMw4YNw8/Pj7Zt2wLQtWtXvL29GTJkCEePHmX79u28/fbbhISEYGNjo9auVWjPermwI7QjwX610Wjgu0O/4z83km3Ga2qXJoqBl5cXLVq0sJh38+ZN7O3tOXbsmHnenTt3aNGiBbNnzy7pEsUjUP2upL8zd+5cevXqRf/+/enQoQNubm6sX7/evFyn07F582Z0Oh1+fn4MHjyYoUOHMm3aNBWrFo42eqYGNWXtK37Ur+7AjZRMRq86zCtfHSJBxpsuNw4cOIBer+fGjRucOnXKPL9atWoEBwezcOFC87zhw4fj4+PDa6+9pkapopA0iiL9GyQnJ+Pk5ERSUpJcbyhiGdm5fPrzeRbvvkBOnkIlWz1vBzZmQCsPNBqN2uWVCenp6Tg6OvLee+/x5ptvql2O2fjx46levTq3b9/GxsaG6dOnm5edPXuWFi1aEBcXx9KlS/nhhx+IjIyUI3kVFeZ3ToIBCYaScOpaMpPWHePY7/mjxLWrX5UZ/XyoXdVB5cpKv5iYGFq1asUPP/xAnz591C4HgJycHGrWrMmePXtISkpiwIABXLp0ySLsg4KCyMvLIzY2lujoaNzd3VWsWBTmd65Un0oS5UfjGvnjTb/VszG2Vlr2XbhFt3l7+GyPjDf9MEajEYAmTZqoXMl/7dixgxo1auDl5UXr1q3RarXs3bvXos348ePZsmUL69atk1AoYyQYRInR67SM6FCP7RM60K5+VTKy85i+9TT9Fu/j1DV5+vxBjEYj9vb21KtXT+1SzL7++msGDhxo/nvAgAF8/fXXFm2ysrKoU6cOrVu3LunyxGOSYBAlrnZVB1b9qw2z+vtSyVbPsd+T6L3wFz7efkbGm74Po9FI48aN2b9/P506dcLBwYFGjRrxzTffqFJPamoqGzduZMCAAeZ5zz//PN9//z2Zmf8ds+PYsWP3dIwpygYJBqEKjUbDgKc82Bnake5N3MjJU/jk5/MELtjLwcsy3vRfGY1GUlNTGThwIF26dDF3Uf/iiy8SHR1d6PVlZ2dz8+bNAk15efee5lu/fj0NGzakZs2aZGRkkJGRQaNGjahatSpbtmwxtzt69CjNmjV7rH0XKinmDv3KBOldVX3bjPFKq/cjzL22vrPBqKRkZKtdlupu3LihAIqzs7Ny6dIl8/y4uDjF2tpaGTlyZKHX+fPPPytAgaa/bvOugICAB7Z/7rnnzO28vb2V77///lF2WxSDwvzO6Us4h4S4r+5Na+BXrxrTt57i20NX+DLqN346eZ0PnvPh2UYuapenmuPHjwPw1ltvUadOHfN8Dw8PvLy8uHTpUqHX2axZMyIiIgrU9n5dy+zYsaNA7/27Ho5F6SbBIEoNJ3srPvyHL32auxO23khc4h2GrThIUHN33u3lTVXHincP/N07kgYPHnzPMq1Wi729PQCLFy/m888/x2g08tZbbzFlypQHrrNKlSr3dHMvxF9JMIhS5+kG1dg+piV2H+d3h9449gv2nrvJ5N7e9GnmXqEejDMajbi7u+PiYnnUdOfOHU6fPm1+rqFGjRpMmTKF1atXP3SdWVlZJCYW7DpO9erV0el0hS9clGkSDKJUsrP+74/RE66VOHo9i1fXxLLhyFU+eM4H98p2KlZXcoxG431/mJcvX05mZqZ5qNu+ffsCsHXr1oeuc9++fTz77LMF2v6lS5csTmEBRRbMijxbW2pJMIhSb+0rfiyNMrFw13l+PnODgDmRTOrRiMFtaqPVlt+jB0VROH78OHfu3OHChQvUr18fgBs3bjBz5kz69OlD8+bNC73ex73GID/o5Z8Egyj1rPVaxnVpSA8fN95YZ+TQb3/w7g8n2HQ0nhn9fGng4qh2icXi8uXLpKam0rJlSwIDAxk/fjx37tzhk08+QavV8tlnnz3SeovyGoOXlxcODg4cPnzYYv7Ro0fp3LkzV65cMV8HGTduHMePHyciIgK9Xn56SjN5jkGUGQ1cKvHdK35M+3O86YOX/6Dn/L18susc2eWwW427F56XLVuGv78/b775Jh988AEdO3Zk//799wxrW9Ie1Lsq5B+VtGjRguXLlwPwxRdfsHnzZtauXSuhUAZIJ3pIJ3qlUlYaTP+zf50348HasrO9q7fTeSvcyO4zNwBo5FaJWf/wxbdW5RIutHQZNWoUbm5uf3tXUlH5u95VAX788UfGjRvHypUr6dWrFz///LM88KYi6URPlHs1K9ux/KWnmDewOVXsrThtSqHvp78yfesp0rMqXrcaOTk5ZGRkkJuba/G6OLf37bffMmDAAAYOHMjq1avvufbQvXt3bG1tCQgIYMmSJRIKZYgEgyidNDpo2DV/0tz/dkmNRkPfJ/PHm+7TLH+86c/2XKT7/D3sq2DjTb///vvY2dmxbNkyPvjgA+zs7Pjqq6+KbXsF6V01MzMTGxsb6tSpY9Gvkij95FQSciqpvNh1+jpvhR/nWlL+KHEDW3nwZmBjnOysVK6s/HnxxRfx8fEhLCwMgDfeeIPExESLC+LBwcEkJyezb98+NmzYYB6rXahDBuopJAmG8iMlI5tZP57hq/2/AeBSyYZpQU3p3vTe2y7Fo0lNTcXNzY2jR4+ab6GNjY2lc+fOXLt2DRsbGz7++GO++uor9u3bx+zZszl27Bjff/+9ypVXbBIMhSTBUP4cuJTIG+uOcfFmGgA9mroxNagJLpVsVa6s7Pvyyy+ZO3cuUVFRFvN9fHz48MMPsbe356WXXmL//v3UqVOHmzdvUrduXY4ePVqqxpSoaCQYCkmCoRTKSoOPGuS/fv38PXclFURGdi4Ld51jaeRFcvIUDLZ63g705p+talWobjWKWteuXR/4gFzfvn355ZdfWLt2LZ06dTLPHzNmDHq9ngULFpRQleJ/STAUkgRDKfSQ21UL42R8/njTxqv5400/06Aa05/zwbOqfVFUKkSZILerCvEX3u4Gwse0I6xHI2z0Wn45f5Nu8/awbO9FcvMq/L+LhLiHBIOoEPQ6La90rM/2CR3wq1eV9Oxc3t9yin6LfuW0ScabFuKvJBhEhVKnmgOrR7RhZj8fKtnqOfp7Er0W/MKcHWfIzKl4D8YJcT8SDKLC0Wg0PN/ak59CO9LV25WcPIUFu84TuOAXYn6T8aaFkGAQFZarwZalQ1qyaFALqjnacD4hlX8siWLyD8dJzcxRuzwhVCPBIEonjRZqP5M/aYrva6rRaOjpU4OfQjvwz5a1UBRYGfUb3ebuYfeZhGLbrhClmdyuityuKv5r77kbhK038vsf6QA892RN3unljbODtcqVCfF45HZVIR5R+4bV2TGxA8OfqYtWA+FHrhIwJ5IfYq/KyGWiwpBgEOJ/2FvreaeXN+vHPI2XayVupeWPN/2vlYe4lpSudnlCFDsJBlE6ZaXBrHr5U1aaKiU096jMpnHPMNH/Cax0GnaeTiBgzh6+2v8befJgnCjHVA2GxYsX4+vri8FgwGAw4Ofnx7Zt28zLTSYTQ4YMwc3NDQcHB1q0aMG6dess1pGYmMigQYMwGAxUrlyZ4cOHk5qaWtK7IorDnVv5k4qs9Vpe9W/I1vHtaeFZmdTMHN7ZcJznP9vPhRvyPRPlk6rBUKtWLWbOnElMTAyHDh2ic+fOBAUFceLECQCGDh3KmTNn2LhxI0ajkX79+jFgwACOHDliXsegQYM4ceIEERERbN68mT179jBy5Ei1dkmUUw1dK7F2VDum9PbG3lrHgcuJ9Ji/l09/Pl8ux5sWFZxSylSpUkVZtmyZoiiK4uDgoHz55ZcWy52dnZXPP/9cURRFOXnypAIoBw8eNC/ftm2botFolKtXrxZ4m0lJSQqgJCUlFcEeiCKRmaookw35U2aq2tVYuJKYpgz5v2il9qTNSu1Jm5Ue8/Yox67cVrssIf5WYX7nSs01htzcXNasWUNaWpp5pKd27drx7bffkpiYSF5eHmvWrCEjI8PcnW9UVBSVK1emVatW5vX4+/uj1WqJjo5WYzdEBVCrij0rhz3FnAHNqGxvxclryfRd9CszKuh406L80atdgNFoxM/Pj4yMDBwdHQkPD8fb2xuA7777joEDB1K1alX0ej329vaEh4fToEF+P/0mkwkXFxeL9en1epydnTGZTA/cZmZmJpmZmea/k5OlEzVROBqNhn4tatHhiepM3XSSTUfjWbrnIttPmJjRzxe/+lXVLlGIR6b6EYOXlxexsbFER0czevRogoODOXnyJADvvPMOt2/f5qeffuLQoUOEhoYyYMAAjEbjY21zxowZODk5mScPD4+i2BVRAVVztGHhC0+ybGgr3Ay2XL51hxc+30/Y+mMkpWerXZ4Qj6TIn3y+evUqNWvWfOT3+/v7U79+ff7zn//QoEEDjh8/TpMmTSyWN2jQgCVLlvDFF1/w2muv8ccff5iX5+TkYGtry9q1a3nuuefuu437HTF4eHjIk8+lSXY6LO+R/3rYNrCyU7eeAkjOyObDbadZFR0H5I83/V7fpnRrIuNNC/Wp8uSzyWRi3LhxNGzY8LHWk5eXR2ZmJnfu3AFAq7UsUafTkZeXfxeIn58ft2/fJiYmxrx8165d5OXl0aZNmwduw8bGxnyL7N1JlDJWdjByd/5UBkIBwGBrxQfP+fDtyLbUreZAQkomr3wVQ8iqw9xIyXz4CoQoJQoVDH/88QcvvPAC1apVw93dnQULFpCXl8e7775LvXr1OHjwIMuXLy/w+sLCwtizZw+XL1/GaDQSFhbG7t27GTRoEI0aNaJBgwa88sorHDhwgAsXLjB79mwiIiLo27cvAI0bN6Z79+6MGDGCAwcO8OuvvzJ27Fief/553N3dC/VBiIdbtGgRGo3mgaEbEBCAg4MDt2/fvmfZ1atXqVGjBq1atSIjI6OYK1VXm3pV2fZqe0Z3qo9Oq2GL8Rr+cyJZe+iKdKshyobC3O40cuRIxdPTU3nttdeUpk2bKlqtVunRo4cSGBioREVFFfr2qZdfflmpXbu2Ym1trVSvXl3p0qWLsmPHDvPys2fPKv369VNcXFwUe3t7xdfX957bV2/duqW88MILiqOjo2IwGJRhw4YpKSkphapDblctmHbt2inW1tYKoJw7d+6e5Vu3blUAZfbs2Rbz09PTlaeeekpxdXVVrly5UlLllgrG328rgQv2mG9tHbxsvxJ3K03tskQFVJjfuUIFg4eHh7Jz505FURTl0qVLikajUcLCwh6tylJEguHhLl68qADKtGnTFCsrK2XKlCn3tMnLy1O8vb2V+vXrK7m5ueb5wcHBipWVlbJ3796CbzAzTVHmNM2fMsv2D2l2Tq6yePd55Ym3tiq1J21WGr29TVm296KSk5undmmiAim25xji4+Np3LgxAHXq1MHW1pbBgwcX7SGMKJVWrVqFTqdj5MiRBAQEsGrVqnvaaDQaJk6cyIULF8xdm8yfP5+VK1eyYMECnnnmmUJsUYGkuPyJsn36Ra/TMqpjfX6c0IE2dZ1Jz87lvc0n6b94H2dMKWqXJ8Q9ChUMiqKg1//30QedToedXdm4MCgez6pVq+jQoQOurq4MGDCAc+fOcfDgwXvaDR48GBcXFxYuXMiuXbv497//zciRIxk1apQKVZcudas58M2Itkx/zodKNnpir9ym18K9zIk4K+NNi1KlUA+4KYpCly5dzOGQnp5O7969sba2HMTk8OHDRVehUF1MTAynT59mwoQJAPTt2xdra2tWrVrFU089ZdHW1taWMWPGMHXqVA4cOECbNm1YuHChClWXTlqthhfbeNK5kQtvbzjOT6eus2DnObYZr/HhP3xp4VlF7RKFKNxzDFOnTi1Qu8mTJz9yQWqQEdz+XmhoKAsXLuTatWtUq1YNgKCgIKKjo7l69So6nc6i/Y0bN6hVqxbOzs7Exsbi6upa+I1mpcH0P+8sezMerB0edzdKHUVR2GK8xpSNJ7iZmoVGA8F+dXi9mxcONqp3SiDKmcL8zhXq21fWfvDF47vbh1Xnzp3NoQAwcOBANm7cyM6dO+natavFexwcHMjJySEwMPDRQqGC0Gg09PJ15+n61Xh/yynWHf6dFfsuE3HyOtP7+dDxiepqlygqqEJdY9i1axc5OTnFVYsohXbt2sW1a9cYOHCgxfw+ffpgZ2d334vQx48fJy8vD19f35Iqs0yr4mDN7AHN+PLl1tSqYsfV2+kEf3GA0O9i+SMtS+3yRAVUqGAICAggMTHR/Hfbtm25evVqkRclSo9Vq1ZhZWV1T/cijo6O9OzZk/DwcNLTLYe7PHbsGMBjBoMGqjfKn9A8xnrKjg5PVGf7hA4Me7oOGg2sP3yVgLmRbDoaLw/GiRJV6LuS/urEiRMWfQ6J8iU9PZ3169cTEBBAlSr3XhQdMGAAKSkpbNy40WJ+kQSDtT2EROdP1vaPvp4yxsFGz+TeTVg3uh1PuDpyMzWLcd8cYcSXMt60KDlyhUs80MaNG0lJyb/PfubMmfcsv9uf1apVqyxONR07doyaNWvi7OxcMoWWQy08q7B5XHsW7T7Ppz+f56dTCURf3MMbPRvxwlOeaLUV4yhKqKNQdyXpdDpMJhPVq+dfFDMYDBw9epS6desWW4ElQe5Kur8+ffqwadOmh7azsrLi2rVrVK2aPwZB1apVadOmDVu3bi3uEiuEs9dT+M/3x4i9chuANnWdmdHPh3rVHdUtTJQphfmdK1QwaLVamjZtan6O4dixYzRq1KjMP8cgwVAKZd2Bz5/Nfz3i5wp1Oul+cvMUVu67zEfbz5CenYu1XssE/4aMaF8PK53qw6qIMqDEblcNCgoqfHVCFIgCN07/93UFp9NqePmZugR4u/JmuJG9524y68czbDl2jQ/7+9K0ppPaJYpypMgH6imL5IihFKoAD7g9KkVRWHf4Ku9tPklSejY6rYYR7esxwb8htla6h69AVEjFNlBPQkLC3y7PycnhwIEDhVmlEKKQNBoN/2hZi59COxLoW4PcPIUlkRfoMX8v+y/eUrs8UQ4UKhhq1KhhEQ4+Pj5cuXLF/PetW7fw8/MruuqEEA9UvZINn77Ygs+HtsLVYMOlm2k8/9l+3gw3kpwh402LR/dYzzFcvnyZ7Ozsv20jhCheAd6uRIR25IXWngCsjo6j65w9RJy8rnJloqwq8tsZNBq5v1qIkmawtWJGPx/WjGxLnar2mJIzGPHlIUJWy3jTovDkPjdRSmnAyTN/qiBdYhSFtvWq8uOEDozq+Od408euETA3knUxv8vRvCiwQt2uqtFoSElJwdbWFkVR0Gg0pKamkpycDGD+XyEem7U9TDSqXUWZZGul440ejejlW4P/fH+Mk9eSeW3tUX44Gs8HfZvi4VyxnwkRD1foB9z+eqrobjj879+5uWVrNCq5XVWUV9m5eXy+9yLzfjpHVk4e9tY6/t3Vi+B2ddBJtxoVSrE9+RwZGVmgdh07dizoKksFCQZR3l24kUrYOiMHLuf3jvykZ2Vm9feloWsllSsTJaXYgiE3N5ePP/6YjRs3kpWVRZcuXZg8eXKZH/dZgqEUyk6H5T3yXw/bBlZl+ztWGuTlKaw+EMfMbadJzczBSqch5NkGjOnUAGu9XG4s74rtAbfp06fz5ptv4ujoSM2aNZk/fz4hISGPVawQ96XkQfyR/EnJU7uackGr1TC4bW0iQjvQpZEL2bkK8346R6+FezkS94fa5YlSpFDB8OWXX7Jo0SK2b9/Ohg0b2LRpE6tWrSIvT/7DFaKsqOFkx7LgVix44UmqOlhz9noq/RbvY9qmk9zJkhEaRSGDIS4ujp49e5r/9vf3R6PREB8fX+SFCSGKj0ajoU8zd34K7Ui/J2uiKPDFr5foOncPe8/dULs8obJCBUNOTg62trYW86ysrO55+lkIUTZUcbBmzsDmrBj2FDUr2/H7H+kM+b8DvPbdUW7fkfGmK6pCPcegKAovvfQSNjY25nkZGRmMGjUKB4f/9n65fv36oqtQCFHsOnm5sH1iBz7efoaVUZdZd/h3Is8mMLVPU3r6uEmPBhVMoYIhODj4nnmDBw8usmKEEOpxtNEzpU8TejdzZ9K6Y5xPSCVk9WECvF15v29TXA22D1+JKBdkPAbkdtVSKSsN5vnkv55glPEYSlhmTi6f/nyBRT+fJydPoZKNnrCejXn+KQ8Zb7qMKrbnGMorCQYh7u+0KZlJ64wc/XO86bb1nJnZz5c61SSoy5pie45BCFGxNHIzsH50O94ObIydlY79FxPpNm8PSyIvkJMrt6mXV6oGw+LFi/H19cVgMGAwGPDz82Pbtm0WbaKioujcuTMODg4YDAY6dOhAenq6eXliYiKDBg3CYDBQuXJlhg8fTmpqaknvihDllk6r4V/t67FjYgeeaVCNzJw8Zm47Td9Fv3IiPknt8kQxUDUYatWqxcyZM4mJieHQoUN07tyZoKAgTpw4AeSHQvfu3enatSsHDhzg4MGDjB07Fq32v2UPGjSIEydOEBERwebNm9mzZw8jR45Ua5dEUclOh+WB+VN2+sPbi2Ln4WzPV8NbM+sfvhhs9Ry/mkyfT35l1o+nycguWx1nir9X6q4xODs789FHHzF8+HDatm1LQEAA77333n3bnjp1Cm9vbw4ePEirVq0A+PHHH+nZsye///477u7uBdqmXGMohbLSYPqf//+9GS8Xn0uZhJQMpmw8wVajCYB61RyY2d+X1nWdVa5MPEiZvMaQm5vLmjVrSEtLw8/Pj4SEBKKjo3FxcaFdu3a4urrSsWNHfvnlF/N7oqKiqFy5sjkUIP9pbK1WS3R0tBq7IUSF4FLJlkWDWrJ0SEtcKtlw8WYaA5ZG8Va4kRQZb7rMUz0YjEYjjo6O2NjYMGrUKMLDw/H29ubixYsATJkyhREjRvDjjz/SokULunTpwrlz5wAwmUy4uLhYrE+v1+Ps7IzJZHrgNjMzM0lOTraYhBCF162JGxGhHXn+KQ8AVkXH0XXuHnaekvGmyzLVg8HLy4vY2Fiio6MZPXo0wcHBnDx50twx3yuvvMKwYcN48sknmTt3Ll5eXnzxxRePtc0ZM2bg5ORknjw8PIpiV4SokJzsrJjZ35fVI9pQu6o915IyGL7yEOO+OcLNVBlvuixSPRisra1p0KABLVu2ZMaMGTRr1oz58+dTo0YNALy9vS3aN27cmLi4OADc3NxISEiwWJ6Tk0NiYiJubm4P3GZYWBhJSUnm6cqVK0W8V0JUPO3qV+PHVzvwSod6aDWw6Wg8AXMiWX9Yxpsua1QPhv+Vl5dHZmYmderUwd3dnTNnzlgsP3v2LLVr1wbAz8+P27dvExMTY16+a9cu8vLyaNOmzQO3YWNjY75F9u4khHh8dtY6wno2ZkPI0zRyq8Qfd7IJ/e4oLy0/yO9/3FG7PFFAheorqaiFhYXRo0cPPD09SUlJYfXq1ezevZvt27ej0Wh4/fXXmTx5Ms2aNaN58+asXLmS06dP8/333wP5Rw/du3dnxIgRLFmyhOzsbMaOHcvzzz9f4DuSRClmJYPWl1W+tSqzadwzfLbnIvN3niPy7A26zt3Df7p5MdSvjnSrUcqpervq8OHD2blzJ9euXcPJyQlfX18mTZpEQECAuc3MmTP59NNPSUxMpFmzZsyaNYtnnnnGvDwxMZGxY8eyadMmtFot/fv3Z8GCBTg6Oha4DrldVYjic+FGKm+sO8bBy/mjxLXwrMyHMt50iZO+kgpJgkGI4pWXp7Aq+jdmbjtNWlYu1jotYzs3YFTH+jLedAkpk88xCCHKL61WwxC/OkSEdqRzIxeycvOYE3GWPp/8QuyfHfSJ0kOCQZRO2Rmw6p/5U3aG2tWIIuJe2Y7/C27F/Oeb4+xgzWlTCv0W/cp7m2W86dJEgkGUTkounNuRPynSD095otFoCGpek59CO9K3uTt5CvzfL5foNm8Pv56/qXZ5AgkGIYRKnB2smff8kywf9hTuTrZcSUxn0LJoXl97lKQ70q2GmiQYhBCqetbLhR2hHQn2q41GA2tjfqfLnEi2Ga+pXVqFJcEghFCdo42eqUFNWfuKH/WrO3AzNZPRqw7zyleHSEiWa0wlTYJBCFFqtKrjzJbx7RnXuQF6rYbtJ67TZU4kaw7ESbcaJUiCQQhRqtha6Xitqxebxj2Dby0nUjJyeGO9kRc/j+byzTS1y6sQJBiEEKVS4xr5402/1bMxtlZaoi7eovv8PXy2R8abLm7y5DPy5LMQpd1vt9IIW29k34VbAPjWcmJmP1+83eW/14KSLjEKSYKhFMvO+PvnGKzsQfNnh2w5mZD3Nw9JFaat3g7uji2ekwV5f3P7ZKHa2oJWV/i2udmQm/Xgtjob0OkfoW0O5P7NmAk6a9BZFb5tXi7k/M1FY60V6K0L1VZRFNYejOPjrbGkZOSg12oY/kxdRgX4Ymule/D7BVC43zlVe1cV4qHCR8LJHx68/K/jQW+aAEdXP7jt6xfAoVr+6+1vwsFlD2776jGokt+9O7umwb6FD247Zj+4NM5/vXc2RM58cNsRu6Bmy/zX0Ysh4t0Htw3eDHXb57+OWQFb//3gti9+B090y3997Dv4YcyD2/5zBTR5Lv/16U2w9qUHtw1aBE8Oyn99YSesHvDgtj0/htYj8l//tg9W9npw24Bp8PSr+a+vxcLnnR/ctuMb8GwYGo2GAXXuMIChYPvnsgPQ+dQPfNjfl6fqyHjTRUWuMQghyrSLN9L455Io3tlwXMabLiJyKgk5lVSqyamk/NdyKunPtnmQk25elHQnm+k/xfHtofxRGGs42fLBc03p3Mj1weuroOQaQyFJMAhRtv16/iZh643EJeaPEhfU3J13e3lT1dFG5cpKD+l2WwhRoTzdoBrbJ3RgRPu6aDXwQ2w8AXP3sOHIVXkw7hFIMAghygU7ax1vBXoTPiZ/vOnEtCwmfBvLyysOcvV2+sNXIMwkGIQQ5Uozj8psHPsMrwU8gbVOy89nbtB1TiRfRl0mL0+OHgpCgkEIUe5Y67WM69KQra8+Q6vaVUjLyuXdH04wYGkU5xNS1S6v1JNgEEKUWw1cKvHdK35MC2qCg7WOQ7/9Qc/5e/lk1zmypVuNB5JgEKKMWLRoERqNhjZt2jywzdSpU9FqtZw6deqeZS+//DI6nY4tW7YUZ5mljlarYahfHXaEdqSTV3WycvP4eMdZei/8hWO/31a7vFJJgkGIMmLVqlVYW1tz4MABzp8/f982o0ePxsbGhnnz5lnMX7hwIcuXL2fatGkEBgaWQLWlT83Kdix/6SnmDWxOFXsrTptS6Pvpr3yw5STpWTJ87F9JMAhRBly6dIl9+/bx9ttvY2VlxapVq+7bzsXFhUGDBvHVV19x82b++MmRkZGEhobSv39/3nrrrZIsu9TRaDT0fTJ/vOmgP8eb/nxv/njT+2S8aTMJBiHKgFWrVqHT6Rg5ciQBAQEPDAaAiRMnkp6ezpIlS7hy5QoDBgygUaNGrFixouQKLuWqOtow//kn+eKlVtRwsiUu8Q4vLotm0vfHSEqXbjXkyWfkyWdR+jVu3JgaNWqwa9cuVq5cyUsvvcSBAwd46qmn7tu+W7duHDt2jJo1a3Lx4kUOHjxI/fr1S7jqsiElI5tZP57hq/2/AVC9kg3vBTWhe9MaKldWtOTJZyHKkZiYGE6fPs3AgQMB6Nu3L9bW1n971BAaGorJZOLIkSN88803Egp/o5KtFe/1bcraUX7Uq+7AjZRMRn19mNFfx5CQUjHHm5ZgEKKUW7VqFXq9nv79+wPg5ORE9+7dWbNmDbm5979oeveupHr16tGtW7cSq7Use6qOM1vHtyfk2frotRq2HTfhPzuS7w5eqXDdakgwCFGK5ebmsmbNGjp37ky1atXM8wcOHMj169fZuXPnPe/56aef+Pe//03Dhg05f/48O3bsKMmSyzRbKx2vd2vExrHP4FPTieSMHP6z7hiD/y+auFt31C6vxEgwCFGK7dq1i2vXrplPI93Vp08f7Ozs7jmddPHiRQYOHMiTTz7JwYMHcXFxYe7cuSVZcrng7W4gfEw7wno0wkav5dfzt+g6L5Jley+SWwG61ZBgEKIUW7VqFVZWVjz33HMW8x0dHenZsyfh4eGkp+d3EJeamkpQUBBWVlaEh4fj5OTEmDFj2L59+30feBN/T6/T8krH+myf0AG/elXJyM7j/S2n6LfoV06bktUur1hJMAhRSqWnp7N+/XoCAgKoUqXKPcsHDBhASkoKGzduRFEUhgwZwpkzZ1i3bh21atUCYMyYMfd94E0UXJ1qDqwe0YaZ/XyoZKvn6O9J9FrwC7N3nCEzp3w+GKdqMCxevBhfX18MBgMGgwE/Pz+2bdt2TztFUejRowcajYYNGzZYLIuLiyMwMBB7e3tcXFx4/fXXycn5m5G5hCgjNm7cSEpKCgAzZ868Zzp+/DiQf1QxZcoUNmzYwCeffMLTTz9tXkf16tUZPHgwX331Fbdu3VJlP8oDjUbD8609+Sm0I129XcnJU1i46zyBC34h5rdEtcsreoqKNm7cqGzZskU5e/ascubMGeXNN99UrKyslOPHj1u0mzNnjtKjRw8FUMLDw83zc3JylKZNmyr+/v7KkSNHlK1btyrVqlVTwsLCClVHUlKSAihJSUlFsVtCFInevXsrwEMnvV6vAMqoUaPuu54TJ04oGo1Gef/990t4D8qnvLw8ZcuxeKXlexFK7UmblTpvbFbe3WBUUjKy1S7tbxXmd67UPeDm7OzMRx99xPDhwwGIjY2lV69eHDp0iBo1ahAeHk7fvn0B2LZtG7169SI+Ph5X1/wxXpcsWcKkSZO4ceMG1tbWBdqmPOAmhCis23ey+GDLKdbG/A7k98X0/nNNedbLReXK7q9MPuB297a8tLQ0/Pz8ALhz5w4vvvgin376KW5ubve8JyoqCh8fH3MoQP4Tn8nJyZw4caLEahdCVDyV7a356J/N+Gp4a2pVsePq7XSGLT/IxG9jSUzLUru8x6J6MBiNRhwdHbGxsWHUqFGEh4fj7e0N5Pf50q5dO4KCgu77XpPJZBEKgPlvk8n0wG1mZmaSnJxsMQkhxKNo37A6OyZ2YPgz+eNNhx+5iv+cSH6ILbvjTevVLsDLy4vY2FiSkpL4/vvvCQ4OJjIykvPnz7Nr1y6OHDlS5NucMWMGU6dOLfL1CiEqJntrPe/08qZ3M3cmfX+MM9dTeHVNLBtj43mvb1PcK9upXWKhlLprDP7+/tSvXx87OzsWLFiAVvvfg5rc3Fy0Wi3t27dn9+7dvPvuu2zcuJHY2Fhzm0uXLlGvXj0OHz7Mk08+ed9tZGZmkpmZaf47OTkZDw8PucYghHhsWTl5LN59gU9+Pkd2roKjjZ5JPRoxqLUnWq1GtbrK5DWGu/Ly8sjMzOSNN97g2LFjxMbGmieAuXPnsnz5cgD8/PwwGo0kJCSY3x8REYHBYDCfjrofGxsb8y2ydychhCgK1notr/o3ZOv49rTwrExqZg7vbDjO85/t58KNsjHetKqnksLCwujRoweenp6kpKSwevVqdu/ezfbt23Fzc7vvBWdPT0/q1q0LQNeuXfH29mbIkCHMmjULk8nE22+/TUhICDY2NiW9O0IIYdbQtRJrR7Xjq6jLzNp+hgOXE+kxfy+vdmnIyA71sNKVun+Xm6laWUJCAkOHDsXLy4suXbpw8OBBtm/fTkBAQIHer9Pp2Lx5MzqdDj8/PwYPHszQoUOZNm1aMVcuhBAPp9NqeOnpuuyY2IEOT1QnKyePj7afoc8nv2L8PUnt8h6o1F1jUIM8xyCEKG6KohB+5CrTNp/k9p1stBoY0b4eE/yfwM5aV+zbL9PXGIQQojzSaDT0a1GLn0I70rtZ/njTS/dcpPv8Pey7ULrGm5ZgEEKIElTN0YaFLzzJsqGtcDPY8tutO7z4eTRh60vPeNMSDEIIoQJ/b1d2hHZgUBtPAL45cIWAOZFsP/Hgh3NLigSDEEKoxGBrxQfP+fDtyLbUreZAQkomr3wVw5hV6o43LcEghBAqa1OvKttebc/oTvXRaTVsNZoImLOHtYfUGW9agkEIIUoBWysdk7o34oeQp2la00BSejavf3+MoV8c4EpiyY43LcEghBClSNOaTmwY8zRv/Dne9N5zN+k6d0+JjjctwSCEEKWMXqdlVMf6/DihA23qOpOencv7W07Rf/E+zphSin37EgxCCFFK1a3mwDcj2jL9OR8q2eiJvXKbXgv3MifibLGONy3BIIQQpZhWq+HFNp5EhHbEv7Er2bkKC3aeo9eCX4j57Y/i2WaxrFUIIUSRcnOy5fOhLfnkxSep5mjNuYRU/rFkH1M2niAtM6dItyXBIIQQZYRGo6GXrzsREzvSv0UtFAVW7LtM17l7iDx7o8i2I8EghBBlTBUHa2YPaMaXL/93vOngLw4Q+m0sfxTBeNMSDEIIUUZ1eKI62yd0YNjTddBoYP2f401vOhr/WA/GSTAIIUQZ5mCjZ3LvJqwb3Y4nXB25lZbFuG+OMOLLQ1xLSn+kdUowCCFEOdDCswqbx7Vngn9DrHQafjqVQNc5e1gV/Rt5hXwwTgbqQQbqEUKUL2evp/Cf748Re+U2AK3rOvNWQG2a168pA/UIIURF9IRrJdaNbse7vbyxs9Jx4FIi/RZHFfj9EgxCCFEO6bQaXn4mf7zp9g2rkZ2TV+D3SjAIIUQ55uFsz5cvt+b9vk0L/B4JBiGEKOc0Gg19n6xZ4PYSDEIIISxIMAghhLAgwSCEEMKCBIMQQggLEgxCCCEsSDAIIYSwIMEghBDCggSDEEIICxIMQgghLEgwCCGEsCDBIIQQwoKqwbB48WJ8fX0xGAwYDAb8/PzYtm0bAImJiYwbNw4vLy/s7Ozw9PRk/PjxJCUlWawjLi6OwMBA7O3tcXFx4fXXXycnJ0eN3RFCiHJBr+bGa9WqxcyZM2nYsCGKorBy5UqCgoI4cuQIiqIQHx/Pxx9/jLe3N7/99hujRo0iPj6e77//HoDc3FwCAwNxc3Nj3759XLt2jaFDh2JlZcX06dPV3DUhhCizSt0Ibs7Oznz00UcMHz78nmVr165l8ODBpKWlodfr2bZtG7169SI+Ph5XV1cAlixZwqRJk7hx4wbW1tYF2qaM4CaEKO8K8ztXaq4x5ObmsmbNGtLS0vDz87tvm7s7pNfnH+hERUXh4+NjDgWAbt26kZyczIkTJx64rczMTJKTky0mIYQQ+VQPBqPRiKOjIzY2NowaNYrw8HC8vb3vaXfz5k3ee+89Ro4caZ5nMpksQgEw/20ymR64zRkzZuDk5GSePDw8imhvhBCi7FM9GLy8vIiNjSU6OprRo0cTHBzMyZMnLdokJycTGBiIt7c3U6ZMeexthoWFkZSUZJ6uXLny2OsUQojyQtWLzwDW1tY0aNAAgJYtW3Lw4EHmz5/P0qVLAUhJSaF79+5UqlSJ8PBwrKyszO91c3PjwIEDFuu7fv26edmD2NjYYGNjU9S7IoQQ5YLqRwz/Ky8vj8zMTCD/SKFr165YW1uzceNGbG1tLdr6+flhNBpJSEgwz4uIiMBgMNz3dJQQQoiHU/WIISwsjB49euDp6UlKSgqrV69m9+7dbN++3RwKd+7c4euvv7a4SFy9enV0Oh1du3bF29ubIUOGMGvWLEwmE2+//TYhISFyRCCEEI9I1WBISEhg6NChXLt2DScnJ3x9fdm+fTsBAQHs3r2b6OhoAPOpprsuXbpEnTp10Ol0bN68mdGjR+Pn54eDgwPBwcFMmzZNjd0RQohyodQ9x6AGeY5BCFHelcnnGIQQQpQOEgxCCCEsSDAIIYSwIMEghBDCggSDEEIICxIMQgghLEgwCCGEsCDBIIQQwoIEgxBCCAsSDEIIISxIMAghhLAgwSCEEMKCBIMQQggLEgxCCCEsSDAIIYSwIMEghBDCggSDEEIICxIMQgghLEgwCCGEsCDBIIQQwoIEgxBCCAsSDEIIISxIMAghhLAgwSCEEMKCBIMQQggLEgxCCCEsSDAIIYSwIMEghBDCggSDEEIICxIMQgghLEgwCCGEsKBqMCxevBhfX18MBgMGgwE/Pz+2bdtmXp6RkUFISAhVq1bF0dGR/v37c/36dYt1xMXFERgYiL29PS4uLrz++uvk5OSU9K4IIUS5oWow1KpVi5kzZxITE8OhQ4fo3LkzQUFBnDhxAoCJEyeyadMm1q5dS2RkJPHx8fTr18/8/tzcXAIDA8nKymLfvn2sXLmSFStW8O6776q1S0IIUfYppUyVKlWUZcuWKbdv31asrKyUtWvXmpedOnVKAZSoqChFURRl69atilarVUwmk7nN4sWLFYPBoGRmZhZ4m0lJSQqgJCUlFd2OCCFEKVKY37lSc40hNzeXNWvWkJaWhp+fHzExMWRnZ+Pv729u06hRIzw9PYmKigIgKioKHx8fXF1dzW26detGcnKy+ajjfjIzM0lOTraYhBBC5FM9GIxGI46OjtjY2DBq1CjCw8Px9vbGZDJhbW1N5cqVLdq7urpiMpkAMJlMFqFwd/ndZQ8yY8YMnJyczJOHh0fR7pQQQpRhqgeDl5cXsbGxREdHM3r0aIKDgzl58mSxbjMsLIykpCTzdOXKlWLdnhBClCV6tQuwtramQYMGALRs2ZKDBw8yf/58Bg4cSFZWFrdv37Y4arh+/Tpubm4AuLm5ceDAAYv13b1r6W6b+7GxscHGxqaI90QIIcoH1Y8Y/ldeXh6ZmZm0bNkSKysrdu7caV525swZ4uLi8PPzA8DPzw+j0UhCQoK5TUREBAaDAW9v7xKvXQghygNVjxjCwsLo0aMHnp6epKSksHr1anbv3s327dtxcnJi+PDhhIaG4uzsjMFgYNy4cfj5+dG2bVsAunbtire3N0OGDGHWrFmYTCbefvttQkJC5IhACCEekarBkJCQwNChQ7l27RpOTk74+vqyfft2AgICAJg7dy5arZb+/fuTmZlJt27dWLRokfn9Op2OzZs3M3r0aPz8/HBwcCA4OJhp06aptUtCCFHmaRRFUdQuQm3Jyck4OTmRlJSEwWBQuxwhhChyhfmdK3XXGIQQQqhLgkEIIYQFCQYhhBAWVH+OoTS4e5lFusYQQpRXd3/fCnJZWYIBuHXrFoB0jSGEKPdSUlJwcnL62zYSDICzszOQP7bDwz6wiiA5ORkPDw+uXLkid2khn8f9yGdiqSx8HoqikJKSgru7+0PbSjAAWm3+pRYnJ6dS+3+qGu4OoCTyyedxL/lMLJX2z6Og//CVi89CCCEsSDAIIYSwIMFAfm+rkydPlv6V/iSfhyX5PO4ln4ml8vZ5SJcYQgghLMgRgxBCCAsSDEIIISxIMAghhLBQ4YPh008/pU6dOtja2tKmTZt7hgqtSKZMmYJGo7GYGjVqpHZZJWbPnj307t0bd3d3NBoNGzZssFiuKArvvvsuNWrUwM7ODn9/f86dO6dOsSXgYZ/HSy+9dM/3pXv37uoUWwJmzJjBU089RaVKlXBxcaFv376cOXPGok1GRgYhISFUrVoVR0dH+vfvbx5uuCyp0MHw7bffEhoayuTJkzl8+DDNmjWjW7duFkOFVjRNmjTh2rVr5umXX35Ru6QSk5aWRrNmzfj000/vu3zWrFksWLCAJUuWEB0djYODA926dSMjI6OEKy0ZD/s8ALp3727xffnmm29KsMKSFRkZSUhICPv37yciIoLs7Gy6du1KWlqauc3EiRPZtGkTa9euJTIykvj4ePr166di1Y9IqcBat26thISEmP/Ozc1V3N3dlRkzZqhYlXomT56sNGvWTO0ySgVACQ8PN/+dl5enuLm5KR999JF53u3btxUbGxvlm2++UaHCkvW/n4eiKEpwcLASFBSkSj2lQUJCggIokZGRiqLkfx+srKyUtWvXmtucOnVKAZSoqCi1ynwkFfaIISsri5iYGPz9/c3ztFot/v7+REVFqViZus6dO4e7uzv16tVj0KBBxMXFqV1SqXDp0iVMJpPF98XJyYk2bdpU6O/L7t27cXFxwcvLi9GjR5s7pKwIkpKSgP/2tRYTE0N2drbFd6RRo0Z4enqWue9IhQ2Gmzdvkpubi6urq8V8V1dXTCaTSlWpq02bNqxYsYIff/yRxYsXc+nSJdq3b09KSorapanu7ndCvi//1b17d7788kt27tzJhx9+SGRkJD169CA3N1ft0opdXl4eEyZM4Omnn6Zp06ZA/nfE2tqaypUrW7Qti98R6URPmPXo0cP82tfXlzZt2lC7dm2+++47hg8frmJlojR6/vnnza99fHzw9fWlfv367N69my5duqhYWfELCQnh+PHj5fYaXIU9YqhWrRo6ne6eOwauX7+Om5ubSlWVLpUrV+aJJ57g/PnzapeiurvfCfm+PFi9evWoVq1auf++jB07ls2bN/Pzzz9Tq1Yt83w3NzeysrK4ffu2Rfuy+B2psMFgbW1Ny5Yt2blzp3leXl4eO3fuxM/PT8XKSo/U1FQuXLhAjRo11C5FdXXr1sXNzc3i+5KcnEx0dLR8X/70+++/c+vWrXL7fVEUhbFjxxIeHs6uXbuoW7euxfKWLVtiZWVl8R05c+YMcXFxZe47UqFPJYWGhhIcHEyrVq1o3bo18+bNIy0tjWHDhqldmir+/e9/07t3b2rXrk18fDyTJ09Gp9PxwgsvqF1aiUhNTbX41+6lS5eIjY3F2dkZT09PJkyYwPvvv0/Dhg2pW7cu77zzDu7u7vTt21e9oovR330ezs7OTJ06lf79++Pm5saFCxf4z3/+Q4MGDejWrZuKVRefkJAQVq9ezQ8//EClSpXM1w2cnJyws7PDycmJ4cOHExoairOzMwaDgXHjxuHn50fbtm1Vrr6Q1L4tSm0LFy5UPD09FWtra6V169bK/v371S5JNQMHDlRq1KihWFtbKzVr1lQGDhyonD9/Xu2ySszPP/+sAPdMwcHBiqLk37L6zjvvKK6uroqNjY3SpUsX5cyZM+oWXYz+7vO4c+eO0rVrV6V69eqKlZWVUrt2bWXEiBGKyWRSu+xic7/PAlCWL19ubpOenq6MGTNGqVKlimJvb68899xzyrVr19Qr+hFJ76pCCCEsVNhrDEIIIe5PgkEIIYQFCQYhhBAWJBiEEEJYkGAQQghhQYJBCCGEBQkGIYQQFiQYhBBCWJBgEEIIYUGCQVRo9xu3uLyPXSzEw1ToTvSEgPwBZ5YvX24xz8bGRqVqHl9WVhbW1tZqlyHKMDliEBWejY0Nbm5uFlOVKlXYvXs31tbW7N2719x21qxZuLi4mMdl6NSpE2PHjmXs2LE4OTlRrVo13nnnHf7aBdkff/zB0KFDqVKlCvb29vTo0YNz586Zl//222/07t2bKlWq4ODgQJMmTdi6dSsAK1asuGdEsA0bNqDRaMx/T5kyhebNm7Ns2TLq1q2Lra0tALdv3+Zf//oX1atXx2Aw0LlzZ44ePVrkn58ofyQYhHiATp06MWHCBIYMGUJSUhJHjhzhnXfeYdmyZRZDfK5cuRK9Xs+BAweYP38+c+bMYdmyZeblL730EocOHWLjxo1ERUWhKAo9e/YkOzsbyO/OOTMzkz179mA0Gvnwww9xdHQsVK3nz59n3bp1rF+/ntjYWAD++c9/kpCQwLZt24iJiaFFixZ06dKFxMTEx/9wRPmmbueuQqgrODhY0el0ioODg8X0wQcfKIqiKJmZmUrz5s2VAQMGKN7e3sqIESMs3t+xY0elcePGSl5ennnepEmTlMaNGyuKoihnz55VAOXXX381L79586ZiZ2enfPfdd4qiKIqPj48yZcqU+9a3fPlyxcnJyWJeeHi48tf/dCdPnqxYWVkpCQkJ5nl79+5VDAaDkpGRYfHe+vXrK0uXLi3oxyMqKLnGICq8Z599lsWLF1vMc3Z2BvJH+lu1ahW+vr7Url2buXPn3vP+tm3bWpza8fPzY/bs2eTm5nLq1Cn0ej1t2rQxL69atSpeXl6cOnUKgPHjxzN69Gh27NiBv78//fv3x9fXt1D7ULt2bapXr27+++jRo6SmplK1alWLdunp6Vy4cKFQ6xYVjwSDqPAcHBxo0KDBA5fv27cPgMTERBITE3FwcCjS7f/rX/+iW7dubNmyhR07djBjxgxmz57NuHHj0Gq1FtcrAPMpqP/dh79KTU2lRo0a7N69+562/3vNQoj/JdcYhPgbFy5cYOLEiXz++ee0adOG4OBg8vLyLNpER0db/L1//34aNmyITqejcePG5OTkWLS5desWZ86cwdvb2zzPw8ODUaNGsX79el577TU+//xzAKpXr05KSgppaWnmtnevIfydFi1aYDKZ0Ov1NGjQwGKqVq3ao3wUogKRYBAVXmZmJiaTyWK6efMmubm5DB48mG7dujFs2DCWL1/OsWPHmD17tsX74+LiCA0N5cyZM3zzzTcsXLiQV199FYCGDRsSFBTEiBEj+OWXXzh69CiDBw+mZs2aBAUFATBhwgS2b9/OpUuXOHz4MD///DONGzcGoE2bNtjb2/Pmm29y4cIFVq9ezYoVKx66T/7+/vj5+dG3b1927NjB5cuX2bdvH2+99RaHDh0q2g9QlD9qX+QQQk3BwcH3HcfXy8tLmTp1qlKjRg3l5s2b5vbr1q1TrK2tldjYWEVR8i8+jxkzRhk1apRiMBiUKlWqKG+++abFxejExERlyJAhipOTk2JnZ6d069ZNOXv2rHn52LFjlfr16ys2NjZK9erVlSFDhlhsMzw8XGnQoIFiZ2en9OrVS/nss8/uufjcrFmze/YtOTlZGTdunOLu7q5YWVkpHh4eyqBBg5S4uLii/AhFOSRjPgvxGDp16kTz5s2ZN2+e2qUIUWTkVJIQQggLEgxCCCEsyKkkIYQQFuSIQQghhAUJBiGEEBYkGIQQQliQYBBCCGFBgkEIIYQFCQYhhBAWJBiEEEJYkGAQQghhQYJBCCGEhf8HbbCjbd82AjcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "ax.set_xlim(0, 23)\n",
    "ax.set_ylim(295, 450)\n",
    "ax.set_xlabel('Exposure')\n",
    "ax.set_ylabel('PEFR')\n",
    "ax.plot((0, 23), model.predict(pd.DataFrame({'Exposure': [0, 23]})))\n",
    "ax.text(0.4, model.intercept_, r'$b_0$', size='larger')\n",
    "\n",
    "x = pd.DataFrame({'Exposure': [7.5,17.5]})\n",
    "y = model.predict(x)\n",
    "ax.plot((7.5, 7.5, 17.5), (y[0], y[1], y[1]), '--')\n",
    "ax.text(5, np.mean(y), r'$\\Delta Y$', size='larger')\n",
    "ax.text(12, y[1] - 10, r'$\\Delta X$', size='larger')\n",
    "ax.text(12, 390, r'$b_1 = \\frac{\\Delta Y}{\\Delta X}$', size='larger')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitted Values and Residuals\n",
    "The method `predict` of a fitted _scikit-learn_ model can be used to predict new data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:07.581443Z",
     "iopub.status.busy": "2022-04-26T19:56:07.581202Z",
     "iopub.status.idle": "2022-04-26T19:56:07.588861Z",
     "shell.execute_reply": "2022-04-26T19:56:07.587630Z"
    }
   },
   "outputs": [],
   "source": [
    "fitted = model.predict(lung[predictors])\n",
    "residuals = lung[outcome] - fitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:07.593671Z",
     "iopub.status.busy": "2022-04-26T19:56:07.592248Z",
     "iopub.status.idle": "2022-04-26T19:56:08.002777Z",
     "shell.execute_reply": "2022-04-26T19:56:08.001812Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = lung.plot.scatter(x='Exposure', y='PEFR', figsize=(4, 4))\n",
    "ax.plot(lung.Exposure, fitted)\n",
    "for x, yactual, yfitted in zip(lung.Exposure, lung.PEFR, fitted): \n",
    "    ax.plot((x, x), (yactual, yfitted), '--', color='C1')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:08.006526Z",
     "iopub.status.busy": "2022-04-26T19:56:08.006290Z",
     "iopub.status.idle": "2022-04-26T19:56:08.093356Z",
     "shell.execute_reply": "2022-04-26T19:56:08.092457Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   AdjSalePrice  SqFtTotLiving  SqFtLot  Bathrooms  Bedrooms  BldgGrade\n",
      "1      300805.0           2400     9373       3.00         6          7\n",
      "2     1076162.0           3764    20156       3.75         4         10\n",
      "3      761805.0           2060    26036       1.75         4          8\n",
      "4      442065.0           3200     8618       3.75         5          7\n",
      "5      297065.0           1720     8620       1.75         4          7\n"
     ]
    }
   ],
   "source": [
    "subset = ['AdjSalePrice', 'SqFtTotLiving', 'SqFtLot', 'Bathrooms', \n",
    "          'Bedrooms', 'BldgGrade']\n",
    "\n",
    "house = pd.read_csv(HOUSE_CSV, sep='\\t')\n",
    "print(house[subset].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:08.096825Z",
     "iopub.status.busy": "2022-04-26T19:56:08.096583Z",
     "iopub.status.idle": "2022-04-26T19:56:08.107587Z",
     "shell.execute_reply": "2022-04-26T19:56:08.106911Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: -521871.368\n",
      "Coefficients:\n",
      " SqFtTotLiving: 228.83060360240793\n",
      " SqFtLot: -0.06046682065307607\n",
      " Bathrooms: -19442.840398321066\n",
      " Bedrooms: -47769.95518521438\n",
      " BldgGrade: 106106.96307898081\n"
     ]
    }
   ],
   "source": [
    "predictors = ['SqFtTotLiving', 'SqFtLot', 'Bathrooms', \n",
    "              'Bedrooms', 'BldgGrade']\n",
    "outcome = 'AdjSalePrice'\n",
    "\n",
    "house_lm = LinearRegression()\n",
    "house_lm.fit(house[predictors], house[outcome])\n",
    "\n",
    "print(f'Intercept: {house_lm.intercept_:.3f}')\n",
    "print('Coefficients:')\n",
    "for name, coef in zip(predictors, house_lm.coef_):\n",
    "    print(f' {name}: {coef}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessing the Model\n",
    "_Scikit-learn_ provides a number of metrics to determine the quality of a model. Here we use the `r2_score`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:08.110572Z",
     "iopub.status.busy": "2022-04-26T19:56:08.110302Z",
     "iopub.status.idle": "2022-04-26T19:56:08.122546Z",
     "shell.execute_reply": "2022-04-26T19:56:08.121440Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 261220\n",
      "r2: 0.5406\n"
     ]
    }
   ],
   "source": [
    "fitted = house_lm.predict(house[predictors])\n",
    "RMSE = np.sqrt(mean_squared_error(house[outcome], fitted))\n",
    "r2 = r2_score(house[outcome], fitted)\n",
    "print(f'RMSE: {RMSE:.0f}')\n",
    "print(f'r2: {r2:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While _scikit-learn_ provides a variety of different metrics, _statsmodels_ provides a more in-depth analysis of the linear regression model. This package has two different ways of specifying the model, one that is similar to _scikit-learn_ and one that allows specifying _R_-style formulas. Here we use the first approach. As _statsmodels_ doesn't add an intercept automaticaly, we need to add a constant column with value 1 to the predictors. We can use the _pandas_ method assign for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:08.127322Z",
     "iopub.status.busy": "2022-04-26T19:56:08.126394Z",
     "iopub.status.idle": "2022-04-26T19:56:08.149840Z",
     "shell.execute_reply": "2022-04-26T19:56:08.148846Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           AdjSalePrice   R-squared:                       0.541\n",
      "Model:                            OLS   Adj. R-squared:                  0.540\n",
      "Method:                 Least Squares   F-statistic:                     5338.\n",
      "Date:                Tue, 23 May 2023   Prob (F-statistic):               0.00\n",
      "Time:                        22:26:09   Log-Likelihood:            -3.1517e+05\n",
      "No. Observations:               22687   AIC:                         6.304e+05\n",
      "Df Residuals:                   22681   BIC:                         6.304e+05\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "SqFtTotLiving   228.8306      3.899     58.694      0.000     221.189     236.472\n",
      "SqFtLot          -0.0605      0.061     -0.988      0.323      -0.180       0.059\n",
      "Bathrooms     -1.944e+04   3625.388     -5.363      0.000   -2.65e+04   -1.23e+04\n",
      "Bedrooms      -4.777e+04   2489.732    -19.187      0.000   -5.27e+04   -4.29e+04\n",
      "BldgGrade      1.061e+05   2396.445     44.277      0.000    1.01e+05    1.11e+05\n",
      "const         -5.219e+05   1.57e+04    -33.342      0.000   -5.53e+05   -4.91e+05\n",
      "==============================================================================\n",
      "Omnibus:                    29676.557   Durbin-Watson:                   1.247\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         19390738.346\n",
      "Skew:                           6.889   Prob(JB):                         0.00\n",
      "Kurtosis:                     145.559   Cond. No.                     2.86e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.86e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(house[outcome], house[predictors].assign(const=1))\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Selection and Stepwise Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:08.155627Z",
     "iopub.status.busy": "2022-04-26T19:56:08.153459Z",
     "iopub.status.idle": "2022-04-26T19:56:08.201070Z",
     "shell.execute_reply": "2022-04-26T19:56:08.200179Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           AdjSalePrice   R-squared:                       0.595\n",
      "Model:                            OLS   Adj. R-squared:                  0.594\n",
      "Method:                 Least Squares   F-statistic:                     2771.\n",
      "Date:                Tue, 23 May 2023   Prob (F-statistic):               0.00\n",
      "Time:                        22:26:09   Log-Likelihood:            -3.1375e+05\n",
      "No. Observations:               22687   AIC:                         6.275e+05\n",
      "Df Residuals:                   22674   BIC:                         6.276e+05\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "SqFtTotLiving                198.6364      4.234     46.920      0.000     190.338     206.934\n",
      "SqFtLot                        0.0771      0.058      1.330      0.184      -0.037       0.191\n",
      "Bathrooms                   4.286e+04   3808.114     11.255      0.000    3.54e+04    5.03e+04\n",
      "Bedrooms                   -5.187e+04   2396.904    -21.638      0.000   -5.66e+04   -4.72e+04\n",
      "BldgGrade                   1.373e+05   2441.242     56.228      0.000    1.32e+05    1.42e+05\n",
      "NbrLivingUnits              5723.8438   1.76e+04      0.326      0.744   -2.87e+04    4.01e+04\n",
      "SqFtFinBasement                7.0611      4.627      1.526      0.127      -2.009      16.131\n",
      "YrBuilt                    -3574.2210     77.228    -46.282      0.000   -3725.593   -3422.849\n",
      "YrRenovated                   -2.5311      3.924     -0.645      0.519     -10.222       5.160\n",
      "NewConstruction            -2489.1122   5936.692     -0.419      0.675   -1.41e+04    9147.211\n",
      "PropertyType_Single Family  2.997e+04   2.61e+04      1.149      0.251   -2.12e+04    8.11e+04\n",
      "PropertyType_Townhouse      9.286e+04    2.7e+04      3.438      0.001    3.99e+04    1.46e+05\n",
      "const                       6.182e+06   1.55e+05     39.902      0.000    5.88e+06    6.49e+06\n",
      "==============================================================================\n",
      "Omnibus:                    31006.128   Durbin-Watson:                   1.393\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         26251977.078\n",
      "Skew:                           7.427   Prob(JB):                         0.00\n",
      "Kurtosis:                     168.984   Cond. No.                     2.98e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.98e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "predictors = ['SqFtTotLiving', 'SqFtLot', 'Bathrooms', 'Bedrooms',\n",
    "              'BldgGrade', 'PropertyType', 'NbrLivingUnits',\n",
    "              'SqFtFinBasement', 'YrBuilt', 'YrRenovated', \n",
    "              'NewConstruction']\n",
    "\n",
    "X = pd.get_dummies(house[predictors], drop_first=True, dtype=int)\n",
    "X['NewConstruction'] = [1 if nc else 0 for nc in X['NewConstruction']]\n",
    "\n",
    "house_full = sm.OLS(house[outcome], X.assign(const=1))\n",
    "results = house_full.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `stepwise_selection` method from the _dmba_ package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:08.207210Z",
     "iopub.status.busy": "2022-04-26T19:56:08.205495Z",
     "iopub.status.idle": "2022-04-26T19:56:08.894313Z",
     "shell.execute_reply": "2022-04-26T19:56:08.893197Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variables: SqFtTotLiving, SqFtLot, Bathrooms, Bedrooms, BldgGrade, NbrLivingUnits, SqFtFinBasement, YrBuilt, YrRenovated, NewConstruction, PropertyType_Single Family, PropertyType_Townhouse\n",
      "Start: score=647988.32, constant\n",
      "Step: score=633013.35, add SqFtTotLiving\n",
      "Step: score=630793.74, add BldgGrade\n",
      "Step: score=628230.29, add YrBuilt\n",
      "Step: score=627784.16, add Bedrooms\n",
      "Step: score=627602.21, add Bathrooms\n",
      "Step: score=627525.65, add PropertyType_Townhouse\n",
      "Step: score=627525.08, add SqFtFinBasement\n",
      "Step: score=627524.98, add PropertyType_Single Family\n",
      "Step: score=627524.98, unchanged None\n",
      "\n",
      "Intercept: 6178645.017\n",
      "Coefficients:\n",
      " SqFtTotLiving: 199.2775530420188\n",
      " BldgGrade: 137159.56022619782\n",
      " YrBuilt: -3565.4249392492984\n",
      " Bedrooms: -51947.38367361323\n",
      " Bathrooms: 42396.16452771774\n",
      " PropertyType_Townhouse: 84479.16203300416\n",
      " SqFtFinBasement: 7.046974967553979\n",
      " PropertyType_Single Family: 22912.055187017646\n"
     ]
    }
   ],
   "source": [
    "y = house[outcome]\n",
    "\n",
    "def train_model(variables):\n",
    "    if len(variables) == 0:\n",
    "        return None\n",
    "    model = LinearRegression()\n",
    "    model.fit(X[variables], y)\n",
    "    return model\n",
    "\n",
    "def score_model(model, variables):\n",
    "    if len(variables) == 0:\n",
    "        return AIC_score(y, [y.mean()] * len(y), model, df=1)\n",
    "    return AIC_score(y, model.predict(X[variables]), model)\n",
    "\n",
    "best_model, best_variables = stepwise_selection(X.columns, train_model, score_model, \n",
    "                                                verbose=True)\n",
    "\n",
    "print()\n",
    "print(f'Intercept: {best_model.intercept_:.3f}')\n",
    "print('Coefficients:')\n",
    "for name, coef in zip(best_variables, best_model.coef_):\n",
    "    print(f' {name}: {coef}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weighted regression\n",
    "We can calculate the Year from the date column using either a list comprehension or the data frame's `apply` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:08.900576Z",
     "iopub.status.busy": "2022-04-26T19:56:08.898874Z",
     "iopub.status.idle": "2022-04-26T19:56:08.940872Z",
     "shell.execute_reply": "2022-04-26T19:56:08.939879Z"
    }
   },
   "outputs": [],
   "source": [
    "house['Year'] = [int(date.split('-')[0]) for date in house.DocumentDate]\n",
    "house['Year'] = house.DocumentDate.apply(lambda d: int(d.split('-')[0]))\n",
    "house['Weight'] = house.Year - 2005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:08.946589Z",
     "iopub.status.busy": "2022-04-26T19:56:08.945702Z",
     "iopub.status.idle": "2022-04-26T19:56:08.980607Z",
     "shell.execute_reply": "2022-04-26T19:56:08.979427Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predictor</th>\n",
       "      <th>house_lm</th>\n",
       "      <th>house_wt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SqFtTotLiving</td>\n",
       "      <td>228.830604</td>\n",
       "      <td>245.024089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SqFtLot</td>\n",
       "      <td>-0.060467</td>\n",
       "      <td>-0.292415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bathrooms</td>\n",
       "      <td>-19442.840398</td>\n",
       "      <td>-26085.970109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bedrooms</td>\n",
       "      <td>-47769.955185</td>\n",
       "      <td>-53608.876436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BldgGrade</td>\n",
       "      <td>106106.963079</td>\n",
       "      <td>115242.434726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>intercept</td>\n",
       "      <td>-521871.368188</td>\n",
       "      <td>-584189.329446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       predictor       house_lm       house_wt\n",
       "0  SqFtTotLiving     228.830604     245.024089\n",
       "1        SqFtLot      -0.060467      -0.292415\n",
       "2      Bathrooms  -19442.840398  -26085.970109\n",
       "3       Bedrooms  -47769.955185  -53608.876436\n",
       "4      BldgGrade  106106.963079  115242.434726\n",
       "0      intercept -521871.368188 -584189.329446"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors = ['SqFtTotLiving', 'SqFtLot', 'Bathrooms', \n",
    "              'Bedrooms', 'BldgGrade']\n",
    "outcome = 'AdjSalePrice'\n",
    "\n",
    "house_wt = LinearRegression()\n",
    "house_wt.fit(house[predictors], house[outcome], sample_weight=house.Weight)\n",
    "\n",
    "pd.concat([\n",
    "    pd.DataFrame({\n",
    "        'predictor': predictors,\n",
    "        'house_lm': house_lm.coef_,\n",
    "        'house_wt': house_wt.coef_,    \n",
    "    }),\n",
    "    pd.DataFrame({\n",
    "        'predictor': ['intercept'],\n",
    "        'house_lm': house_lm.intercept_,\n",
    "        'house_wt': house_wt.intercept_,    \n",
    "    })\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:08.985129Z",
     "iopub.status.busy": "2022-04-26T19:56:08.984834Z",
     "iopub.status.idle": "2022-04-26T19:56:09.025619Z",
     "shell.execute_reply": "2022-04-26T19:56:09.024453Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   abs_residual_lm  abs_residual_wt  Year\n",
      "1    123750.814194    107108.553965  2014\n",
      "2     59145.413089     96191.882094  2006\n",
      "3    190108.725716    187004.492880  2007\n",
      "4    198788.774412    196132.996857  2008\n",
      "5     91774.996129     84277.577512  2013\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>mean abs_residual_lm</th>\n",
       "      <th>mean abs_residual_wt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>140540.303585</td>\n",
       "      <td>146557.454636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007</td>\n",
       "      <td>147747.577959</td>\n",
       "      <td>152848.523235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>142086.905943</td>\n",
       "      <td>146360.411668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>147016.720883</td>\n",
       "      <td>151182.924825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>163267.674885</td>\n",
       "      <td>166364.476152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011</td>\n",
       "      <td>169937.385744</td>\n",
       "      <td>172950.876028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>169506.670053</td>\n",
       "      <td>171874.424266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>203659.777510</td>\n",
       "      <td>206242.199403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014</td>\n",
       "      <td>184452.840665</td>\n",
       "      <td>186668.573750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015</td>\n",
       "      <td>172323.435147</td>\n",
       "      <td>169842.742053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  mean abs_residual_lm  mean abs_residual_wt\n",
       "0  2006         140540.303585         146557.454636\n",
       "1  2007         147747.577959         152848.523235\n",
       "2  2008         142086.905943         146360.411668\n",
       "3  2009         147016.720883         151182.924825\n",
       "4  2010         163267.674885         166364.476152\n",
       "5  2011         169937.385744         172950.876028\n",
       "6  2012         169506.670053         171874.424266\n",
       "7  2013         203659.777510         206242.199403\n",
       "8  2014         184452.840665         186668.573750\n",
       "9  2015         172323.435147         169842.742053"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals = pd.DataFrame({\n",
    "    'abs_residual_lm': np.abs(house_lm.predict(house[predictors]) - house[outcome]),\n",
    "    'abs_residual_wt': np.abs(house_wt.predict(house[predictors]) - house[outcome]),\n",
    "    'Year': house['Year'],\n",
    "})\n",
    "print(residuals.head())\n",
    "# axes = residuals.boxplot(['abs_residual_lm', 'abs_residual_wt'], by='Year', figsize=(10, 4))\n",
    "# axes[0].set_ylim(0, 300000)\n",
    "\n",
    "pd.DataFrame(([year, np.mean(group['abs_residual_lm']), np.mean(group['abs_residual_wt'])] \n",
    "              for year, group in residuals.groupby('Year')),\n",
    "             columns=['Year', 'mean abs_residual_lm', 'mean abs_residual_wt'])\n",
    "# for year, group in residuals.groupby('Year'):\n",
    "#     print(year, np.mean(group['abs_residual_lm']), np.mean(group['abs_residual_wt']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Factor variables in regression\n",
    "## Dummy Variables Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.030898Z",
     "iopub.status.busy": "2022-04-26T19:56:09.030053Z",
     "iopub.status.idle": "2022-04-26T19:56:09.038247Z",
     "shell.execute_reply": "2022-04-26T19:56:09.037195Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1        Multiplex\n",
      "2    Single Family\n",
      "3    Single Family\n",
      "4    Single Family\n",
      "5    Single Family\n",
      "Name: PropertyType, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(house.PropertyType.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.044672Z",
     "iopub.status.busy": "2022-04-26T19:56:09.043079Z",
     "iopub.status.idle": "2022-04-26T19:56:09.052748Z",
     "shell.execute_reply": "2022-04-26T19:56:09.051719Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Multiplex  Single Family  Townhouse\n",
      "1       True          False      False\n",
      "2      False           True      False\n",
      "3      False           True      False\n",
      "4      False           True      False\n",
      "5      False           True      False\n",
      "6      False          False       True\n"
     ]
    }
   ],
   "source": [
    "print(pd.get_dummies(house['PropertyType']).head(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.057059Z",
     "iopub.status.busy": "2022-04-26T19:56:09.056800Z",
     "iopub.status.idle": "2022-04-26T19:56:09.070761Z",
     "shell.execute_reply": "2022-04-26T19:56:09.069553Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Single Family  Townhouse\n",
      "1          False      False\n",
      "2           True      False\n",
      "3           True      False\n",
      "4           True      False\n",
      "5           True      False\n",
      "6          False       True\n"
     ]
    }
   ],
   "source": [
    "print(pd.get_dummies(house['PropertyType'], drop_first=True).head(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.075661Z",
     "iopub.status.busy": "2022-04-26T19:56:09.075411Z",
     "iopub.status.idle": "2022-04-26T19:56:09.091280Z",
     "shell.execute_reply": "2022-04-26T19:56:09.090627Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: -446841.366\n",
      "Coefficients:\n",
      " SqFtTotLiving: 223.37362892503828\n",
      " SqFtLot: -0.07036798136813083\n",
      " Bathrooms: -15979.013473415205\n",
      " Bedrooms: -50889.73218483025\n",
      " BldgGrade: 109416.30516146179\n",
      " PropertyType_Single Family: -84678.21629549257\n",
      " PropertyType_Townhouse: -115121.97921609184\n"
     ]
    }
   ],
   "source": [
    "predictors = ['SqFtTotLiving', 'SqFtLot', 'Bathrooms', 'Bedrooms',\n",
    "              'BldgGrade', 'PropertyType']\n",
    "\n",
    "X = pd.get_dummies(house[predictors], drop_first=True)\n",
    "\n",
    "house_lm_factor = LinearRegression()\n",
    "house_lm_factor.fit(X, house[outcome])\n",
    "\n",
    "print(f'Intercept: {house_lm_factor.intercept_:.3f}')\n",
    "print('Coefficients:')\n",
    "for name, coef in zip(X.columns, house_lm_factor.coef_):\n",
    "    print(f' {name}: {coef}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Factor Variables with many levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.095028Z",
     "iopub.status.busy": "2022-04-26T19:56:09.094649Z",
     "iopub.status.idle": "2022-04-26T19:56:09.105305Z",
     "shell.execute_reply": "2022-04-26T19:56:09.104193Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZipCode  98038  98103  98042  98115  98117  98052  98034  98033  98059  98074   \n",
      "count      788    671    641    620    619    614    575    517    513    502  \\\n",
      "\n",
      "ZipCode  ...  98051  98024  98354  98050  98057  98288  98224  98068  98113   \n",
      "count    ...     32     31      9      7      4      4      3      1      1  \\\n",
      "\n",
      "ZipCode  98043  \n",
      "count        1  \n",
      "\n",
      "[1 rows x 80 columns]\n"
     ]
    }
   ],
   "source": [
    "print(pd.DataFrame(house['ZipCode'].value_counts()).transpose())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.110270Z",
     "iopub.status.busy": "2022-04-26T19:56:09.109620Z",
     "iopub.status.idle": "2022-04-26T19:56:09.217143Z",
     "shell.execute_reply": "2022-04-26T19:56:09.216274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZipGroup\n",
      "0    16\n",
      "1    16\n",
      "2    16\n",
      "3    16\n",
      "4    16\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "house = pd.read_csv(HOUSE_CSV, sep='\\t')\n",
    "\n",
    "predictors = ['SqFtTotLiving', 'SqFtLot', 'Bathrooms', \n",
    "              'Bedrooms', 'BldgGrade']\n",
    "outcome = 'AdjSalePrice'\n",
    "\n",
    "house_lm = LinearRegression()\n",
    "house_lm.fit(house[predictors], house[outcome])\n",
    "\n",
    "\n",
    "zip_groups = pd.DataFrame([\n",
    "    *pd.DataFrame({\n",
    "        'ZipCode': house['ZipCode'],\n",
    "        'residual' : house[outcome] - house_lm.predict(house[predictors]),\n",
    "    })\n",
    "    .groupby(['ZipCode'])\n",
    "    .apply(lambda x: {\n",
    "        'ZipCode': x.iloc[0,0],\n",
    "        'count': len(x),\n",
    "        'median_residual': x.residual.median()\n",
    "    })\n",
    "]).sort_values('median_residual')\n",
    "zip_groups['cum_count'] = np.cumsum(zip_groups['count'])\n",
    "zip_groups['ZipGroup'] = pd.qcut(zip_groups['cum_count'], 5, labels=False, retbins=False)\n",
    "zip_groups.head()\n",
    "print(zip_groups.ZipGroup.value_counts().sort_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.220428Z",
     "iopub.status.busy": "2022-04-26T19:56:09.220087Z",
     "iopub.status.idle": "2022-04-26T19:56:09.228817Z",
     "shell.execute_reply": "2022-04-26T19:56:09.227993Z"
    }
   },
   "outputs": [],
   "source": [
    "to_join = zip_groups[['ZipCode', 'ZipGroup']].set_index('ZipCode')\n",
    "house = house.join(to_join, on='ZipCode')\n",
    "house['ZipGroup'] = house['ZipGroup'].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpreting the Regression Equation\n",
    "## Correlated predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results from the stepwise regression are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.232380Z",
     "iopub.status.busy": "2022-04-26T19:56:09.232033Z",
     "iopub.status.idle": "2022-04-26T19:56:09.238291Z",
     "shell.execute_reply": "2022-04-26T19:56:09.237482Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: 6178645.017\n",
      "Coefficients:\n",
      " SqFtTotLiving: 199.2775530420188\n",
      " BldgGrade: 137159.56022619782\n",
      " YrBuilt: -3565.4249392492984\n",
      " Bedrooms: -51947.38367361323\n",
      " Bathrooms: 42396.16452771774\n",
      " PropertyType_Townhouse: 84479.16203300416\n",
      " SqFtFinBasement: 7.046974967553979\n",
      " PropertyType_Single Family: 22912.055187017646\n"
     ]
    }
   ],
   "source": [
    "print(f'Intercept: {best_model.intercept_:.3f}')\n",
    "print('Coefficients:')\n",
    "for name, coef in zip(best_variables, best_model.coef_):\n",
    "    print(f' {name}: {coef}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.241911Z",
     "iopub.status.busy": "2022-04-26T19:56:09.241545Z",
     "iopub.status.idle": "2022-04-26T19:56:09.256464Z",
     "shell.execute_reply": "2022-04-26T19:56:09.255645Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: 4913973.344\n",
      "Coefficients:\n",
      " Bedrooms: 27150.537230215377\n",
      " BldgGrade: 248997.79366213758\n",
      " YrBuilt: -3211.7448621550866\n",
      " PropertyType_Single Family: -19898.495340502435\n",
      " PropertyType_Townhouse: -47355.4368733449\n"
     ]
    }
   ],
   "source": [
    "predictors = ['Bedrooms', 'BldgGrade', 'PropertyType', 'YrBuilt']\n",
    "outcome = 'AdjSalePrice'\n",
    "\n",
    "X = pd.get_dummies(house[predictors], drop_first=True)\n",
    "\n",
    "reduced_lm = LinearRegression()\n",
    "reduced_lm.fit(X, house[outcome])\n",
    "\n",
    "\n",
    "print(f'Intercept: {reduced_lm.intercept_:.3f}')\n",
    "print('Coefficients:')\n",
    "for name, coef in zip(X.columns, reduced_lm.coef_):\n",
    "    print(f' {name}: {coef}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confounding variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.259914Z",
     "iopub.status.busy": "2022-04-26T19:56:09.259390Z",
     "iopub.status.idle": "2022-04-26T19:56:09.276840Z",
     "shell.execute_reply": "2022-04-26T19:56:09.276043Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: -666637.469\n",
      "Coefficients:\n",
      " SqFtTotLiving: 210.61266005580154\n",
      " SqFtLot: 0.45498713854658845\n",
      " Bathrooms: 5928.42564000163\n",
      " Bedrooms: -41682.87184074476\n",
      " BldgGrade: 98541.18352725972\n",
      " PropertyType_Single Family: 19323.6252879192\n",
      " PropertyType_Townhouse: -78198.720927624\n",
      " ZipGroup_1: 53317.17330659817\n",
      " ZipGroup_2: 116251.58883563551\n",
      " ZipGroup_3: 178360.53178793378\n",
      " ZipGroup_4: 338408.60185652034\n"
     ]
    }
   ],
   "source": [
    "predictors = ['SqFtTotLiving', 'SqFtLot', 'Bathrooms', 'Bedrooms',\n",
    "              'BldgGrade', 'PropertyType', 'ZipGroup']\n",
    "outcome = 'AdjSalePrice'\n",
    "\n",
    "X = pd.get_dummies(house[predictors], drop_first=True)\n",
    "\n",
    "confounding_lm = LinearRegression()\n",
    "confounding_lm.fit(X, house[outcome])\n",
    "\n",
    "print(f'Intercept: {confounding_lm.intercept_:.3f}')\n",
    "print('Coefficients:')\n",
    "for name, coef in zip(X.columns, confounding_lm.coef_):\n",
    "    print(f' {name}: {coef}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactions and Main Effects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.280114Z",
     "iopub.status.busy": "2022-04-26T19:56:09.279683Z",
     "iopub.status.idle": "2022-04-26T19:56:09.383306Z",
     "shell.execute_reply": "2022-04-26T19:56:09.382454Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           AdjSalePrice   R-squared:                       0.682\n",
      "Model:                            OLS   Adj. R-squared:                  0.682\n",
      "Method:                 Least Squares   F-statistic:                     3247.\n",
      "Date:                Tue, 23 May 2023   Prob (F-statistic):               0.00\n",
      "Time:                        22:26:11   Log-Likelihood:            -3.1098e+05\n",
      "No. Observations:               22687   AIC:                         6.220e+05\n",
      "Df Residuals:                   22671   BIC:                         6.221e+05\n",
      "Df Model:                          15                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================================\n",
      "                                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------\n",
      "Intercept                     -4.853e+05   2.05e+04    -23.701      0.000   -5.25e+05   -4.45e+05\n",
      "ZipGroup[T.1]                 -1.113e+04   1.34e+04     -0.830      0.407   -3.74e+04    1.52e+04\n",
      "ZipGroup[T.2]                  2.032e+04   1.18e+04      1.717      0.086   -2877.441    4.35e+04\n",
      "ZipGroup[T.3]                   2.05e+04   1.21e+04      1.697      0.090   -3180.870    4.42e+04\n",
      "ZipGroup[T.4]                 -1.499e+05   1.13e+04    -13.285      0.000   -1.72e+05   -1.28e+05\n",
      "PropertyType[T.Single Family]  1.357e+04   1.39e+04      0.975      0.330   -1.37e+04    4.09e+04\n",
      "PropertyType[T.Townhouse]     -5.884e+04   1.51e+04     -3.888      0.000   -8.85e+04   -2.92e+04\n",
      "SqFtTotLiving                   114.7650      4.863     23.600      0.000     105.233     124.297\n",
      "SqFtTotLiving:ZipGroup[T.1]      32.6043      5.712      5.708      0.000      21.409      43.799\n",
      "SqFtTotLiving:ZipGroup[T.2]      41.7822      5.187      8.056      0.000      31.616      51.948\n",
      "SqFtTotLiving:ZipGroup[T.3]      69.3415      5.619     12.341      0.000      58.329      80.354\n",
      "SqFtTotLiving:ZipGroup[T.4]     226.6836      4.820     47.032      0.000     217.237     236.131\n",
      "SqFtLot                           0.6869      0.052     13.296      0.000       0.586       0.788\n",
      "Bathrooms                     -3619.4533   3202.296     -1.130      0.258   -9896.174    2657.267\n",
      "Bedrooms                       -4.18e+04   2120.279    -19.715      0.000    -4.6e+04   -3.76e+04\n",
      "BldgGrade                      1.047e+05   2069.472     50.592      0.000    1.01e+05    1.09e+05\n",
      "==============================================================================\n",
      "Omnibus:                    30927.394   Durbin-Watson:                   1.581\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         34361794.502\n",
      "Skew:                           7.279   Prob(JB):                         0.00\n",
      "Kurtosis:                     193.101   Cond. No.                     5.80e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.8e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model = smf.ols(formula='AdjSalePrice ~  SqFtTotLiving*ZipGroup + SqFtLot + ' +\n",
    "     'Bathrooms + Bedrooms + BldgGrade + PropertyType', data=house)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Results differ from R due to different binning. Enforcing the same binning gives identical results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the Assumptions: Regression Diagnostics\n",
    "## Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The _statsmodels_ package has the most developed support for outlier analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.387297Z",
     "iopub.status.busy": "2022-04-26T19:56:09.386934Z",
     "iopub.status.idle": "2022-04-26T19:56:09.404017Z",
     "shell.execute_reply": "2022-04-26T19:56:09.403142Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           AdjSalePrice   R-squared:                       0.795\n",
      "Model:                            OLS   Adj. R-squared:                  0.792\n",
      "Method:                 Least Squares   F-statistic:                     238.7\n",
      "Date:                Tue, 23 May 2023   Prob (F-statistic):          1.69e-103\n",
      "Time:                        22:26:11   Log-Likelihood:                -4226.0\n",
      "No. Observations:                 313   AIC:                             8464.\n",
      "Df Residuals:                     307   BIC:                             8486.\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "SqFtTotLiving   209.6023     24.408      8.587      0.000     161.574     257.631\n",
      "SqFtLot          38.9333      5.330      7.305      0.000      28.445      49.421\n",
      "Bathrooms      2282.2641      2e+04      0.114      0.909    -3.7e+04    4.16e+04\n",
      "Bedrooms      -2.632e+04   1.29e+04     -2.043      0.042   -5.17e+04    -973.867\n",
      "BldgGrade        1.3e+05   1.52e+04      8.533      0.000       1e+05     1.6e+05\n",
      "const         -7.725e+05   9.83e+04     -7.861      0.000   -9.66e+05   -5.79e+05\n",
      "==============================================================================\n",
      "Omnibus:                       82.127   Durbin-Watson:                   1.508\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              586.561\n",
      "Skew:                           0.859   Prob(JB):                    4.26e-128\n",
      "Kurtosis:                       9.483   Cond. No.                     5.63e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.63e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "house_98105 = house.loc[house['ZipCode'] == 98105, ]\n",
    "\n",
    "predictors = ['SqFtTotLiving', 'SqFtLot', 'Bathrooms', 'Bedrooms',\n",
    "              'BldgGrade']\n",
    "outcome = 'AdjSalePrice'\n",
    "\n",
    "house_outlier = sm.OLS(house_98105[outcome], house_98105[predictors].assign(const=1))\n",
    "result_98105 = house_outlier.fit()\n",
    "print(result_98105.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `OLSInfluence` class is initialized with the OLS regression results and gives access to a number of usefule properties. Here we use the studentized residuals. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.407982Z",
     "iopub.status.busy": "2022-04-26T19:56:09.407593Z",
     "iopub.status.idle": "2022-04-26T19:56:09.413264Z",
     "shell.execute_reply": "2022-04-26T19:56:09.412699Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24333 -4.326731804078564\n"
     ]
    }
   ],
   "source": [
    "influence = OLSInfluence(result_98105)\n",
    "sresiduals = influence.resid_studentized_internal\n",
    "\n",
    "print(sresiduals.idxmin(), sresiduals.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.416547Z",
     "iopub.status.busy": "2022-04-26T19:56:09.416168Z",
     "iopub.status.idle": "2022-04-26T19:56:09.421280Z",
     "shell.execute_reply": "2022-04-26T19:56:09.420293Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-757753.6192115829\n"
     ]
    }
   ],
   "source": [
    "print(result_98105.resid.loc[sresiduals.idxmin()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.425360Z",
     "iopub.status.busy": "2022-04-26T19:56:09.424846Z",
     "iopub.status.idle": "2022-04-26T19:56:09.432283Z",
     "shell.execute_reply": "2022-04-26T19:56:09.431700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdjSalePrice 119748.0\n",
      "SqFtTotLiving    2900\n",
      "SqFtLot          7276\n",
      "Bathrooms         3.0\n",
      "Bedrooms            6\n",
      "BldgGrade           7\n",
      "Name: 24333, dtype: object\n"
     ]
    }
   ],
   "source": [
    "outlier = house_98105.loc[sresiduals.idxmin(), :]\n",
    "print('AdjSalePrice', outlier[outcome])\n",
    "print(outlier[predictors])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Influential values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.435459Z",
     "iopub.status.busy": "2022-04-26T19:56:09.435106Z",
     "iopub.status.idle": "2022-04-26T19:56:09.566921Z",
     "shell.execute_reply": "2022-04-26T19:56:09.566008Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from scipy.stats import linregress\n",
    "\n",
    "np.random.seed(5)\n",
    "x = np.random.normal(size=25)\n",
    "y = -x / 5 + np.random.normal(size=25)\n",
    "x[0] = 8\n",
    "y[0] = 8\n",
    "\n",
    "def abline(slope, intercept, ax):\n",
    "    \"\"\"Calculate coordinates of a line based on slope and intercept\"\"\"\n",
    "    x_vals = np.array(ax.get_xlim())\n",
    "    return (x_vals, intercept + slope * x_vals)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "ax.scatter(x, y)\n",
    "slope, intercept, _, _, _ = linregress(x, y)\n",
    "ax.plot(*abline(slope, intercept, ax))\n",
    "slope, intercept, _, _, _ = linregress(x[1:], y[1:])\n",
    "ax.plot(*abline(slope, intercept, ax), '--')\n",
    "ax.set_xlim(-2.5, 8.5)\n",
    "ax.set_ylim(-2.5, 8.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The package _statsmodel_ provides a number of plots to analyze the data point influence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.570315Z",
     "iopub.status.busy": "2022-04-26T19:56:09.570144Z",
     "iopub.status.idle": "2022-04-26T19:56:09.711140Z",
     "shell.execute_reply": "2022-04-26T19:56:09.710365Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "influence = OLSInfluence(result_98105)\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.axhline(-2.5, linestyle='--', color='C1')\n",
    "ax.axhline(2.5, linestyle='--', color='C1')\n",
    "ax.scatter(influence.hat_matrix_diag, influence.resid_studentized_internal, \n",
    "           s=1000 * np.sqrt(influence.cooks_distance[0]),\n",
    "           alpha=0.5)\n",
    "\n",
    "ax.set_xlabel('hat values')\n",
    "ax.set_ylabel('studentized residuals')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.714179Z",
     "iopub.status.busy": "2022-04-26T19:56:09.713991Z",
     "iopub.status.idle": "2022-04-26T19:56:09.728517Z",
     "shell.execute_reply": "2022-04-26T19:56:09.727583Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Original</th>\n",
       "      <th>Influential removed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SqFtTotLiving</th>\n",
       "      <td>209.602346</td>\n",
       "      <td>230.052569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SqFtLot</th>\n",
       "      <td>38.933315</td>\n",
       "      <td>33.141600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bathrooms</th>\n",
       "      <td>2282.264145</td>\n",
       "      <td>-16131.879785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bedrooms</th>\n",
       "      <td>-26320.268796</td>\n",
       "      <td>-22887.865318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgGrade</th>\n",
       "      <td>130000.099737</td>\n",
       "      <td>114870.559737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>-772549.862447</td>\n",
       "      <td>-647137.096716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Original  Influential removed\n",
       "SqFtTotLiving     209.602346           230.052569\n",
       "SqFtLot            38.933315            33.141600\n",
       "Bathrooms        2282.264145        -16131.879785\n",
       "Bedrooms       -26320.268796        -22887.865318\n",
       "BldgGrade      130000.099737        114870.559737\n",
       "const         -772549.862447       -647137.096716"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = [dist < .08 for dist in influence.cooks_distance[0]]\n",
    "house_infl = house_98105.loc[mask]\n",
    "\n",
    "ols_infl = sm.OLS(house_infl[outcome], house_infl[predictors].assign(const=1))\n",
    "result_infl = ols_infl.fit()\n",
    "\n",
    "pd.DataFrame({\n",
    "    'Original': result_98105.params,\n",
    "    'Influential removed': result_infl.params,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heteroskedasticity, Non-Normality and Correlated Errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `regplot` in _seaborn_ allows adding a lowess smoothing line to the scatterplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.731644Z",
     "iopub.status.busy": "2022-04-26T19:56:09.731416Z",
     "iopub.status.idle": "2022-04-26T19:56:09.882987Z",
     "shell.execute_reply": "2022-04-26T19:56:09.881840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "sns.regplot(x=result_98105.fittedvalues, y=np.abs(result_98105.resid), \n",
    "            scatter_kws={'alpha': 0.25},\n",
    "            line_kws={'color': 'C1'},\n",
    "            lowess=True, ax=ax)\n",
    "ax.set_xlabel('predicted')\n",
    "ax.set_ylabel('abs(residual)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:09.886052Z",
     "iopub.status.busy": "2022-04-26T19:56:09.885867Z",
     "iopub.status.idle": "2022-04-26T19:56:10.014810Z",
     "shell.execute_reply": "2022-04-26T19:56:10.014137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "pd.Series(influence.resid_studentized_internal).hist(ax=ax)\n",
    "ax.set_xlabel('std. residual')\n",
    "ax.set_ylabel('Frequency')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Partial Residual Plots and Nonlinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:10.017767Z",
     "iopub.status.busy": "2022-04-26T19:56:10.017593Z",
     "iopub.status.idle": "2022-04-26T19:56:10.151978Z",
     "shell.execute_reply": "2022-04-26T19:56:10.151139Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "fig = sm.graphics.plot_ccpr(result_98105, 'SqFtTotLiving', ax=ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:10.154840Z",
     "iopub.status.busy": "2022-04-26T19:56:10.154680Z",
     "iopub.status.idle": "2022-04-26T19:56:10.620170Z",
     "shell.execute_reply": "2022-04-26T19:56:10.619489Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 12))\n",
    "fig = sm.graphics.plot_ccpr_grid(result_98105, fig=fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Polynomial and Spline Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:10.623081Z",
     "iopub.status.busy": "2022-04-26T19:56:10.622864Z",
     "iopub.status.idle": "2022-04-26T19:56:10.645334Z",
     "shell.execute_reply": "2022-04-26T19:56:10.644553Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           AdjSalePrice   R-squared:                       0.806\n",
      "Model:                            OLS   Adj. R-squared:                  0.802\n",
      "Method:                 Least Squares   F-statistic:                     211.6\n",
      "Date:                Tue, 23 May 2023   Prob (F-statistic):          9.95e-106\n",
      "Time:                        22:26:12   Log-Likelihood:                -4217.9\n",
      "No. Observations:                 313   AIC:                             8450.\n",
      "Df Residuals:                     306   BIC:                             8476.\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                  -6.159e+05   1.03e+05     -5.953      0.000   -8.19e+05   -4.12e+05\n",
      "SqFtTotLiving                  7.4521     55.418      0.134      0.893    -101.597     116.501\n",
      "np.power(SqFtTotLiving, 2)     0.0388      0.010      4.040      0.000       0.020       0.058\n",
      "SqFtLot                       32.5594      5.436      5.990      0.000      21.863      43.256\n",
      "Bathrooms                  -1435.1231   1.95e+04     -0.074      0.941   -3.99e+04     3.7e+04\n",
      "Bedrooms                   -9191.9441   1.33e+04     -0.693      0.489   -3.53e+04    1.69e+04\n",
      "BldgGrade                   1.357e+05   1.49e+04      9.087      0.000    1.06e+05    1.65e+05\n",
      "==============================================================================\n",
      "Omnibus:                       75.161   Durbin-Watson:                   1.625\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              637.978\n",
      "Skew:                           0.699   Prob(JB):                    2.92e-139\n",
      "Kurtosis:                       9.853   Cond. No.                     7.37e+07\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 7.37e+07. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model_poly = smf.ols(formula='AdjSalePrice ~  SqFtTotLiving + np.power(SqFtTotLiving, 2) + ' + \n",
    "                'SqFtLot + Bathrooms + Bedrooms + BldgGrade', data=house_98105)\n",
    "result_poly = model_poly.fit()\n",
    "print(result_poly.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The statsmodels implementation of a partial residual plot works only for linear term. Here is an implementation of a partial residual plot that, while inefficient, works for the polynomial regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:10.647987Z",
     "iopub.status.busy": "2022-04-26T19:56:10.647827Z",
     "iopub.status.idle": "2022-04-26T19:56:10.826999Z",
     "shell.execute_reply": "2022-04-26T19:56:10.826183Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0387912816823115\n"
     ]
    }
   ],
   "source": [
    "def partialResidualPlot(model, df, outcome, feature, ax):\n",
    "    y_pred = model.predict(df)\n",
    "    copy_df = df.copy()\n",
    "    for c in copy_df.columns:\n",
    "        if c == feature:\n",
    "            continue\n",
    "        copy_df[c] = 0.0\n",
    "    feature_prediction = model.predict(copy_df)\n",
    "    results = pd.DataFrame({\n",
    "        'feature': df[feature],\n",
    "        'residual': df[outcome] - y_pred,\n",
    "        'ypartial': feature_prediction - model.params[0],\n",
    "    })\n",
    "    results = results.sort_values(by=['feature'])\n",
    "    smoothed = sm.nonparametric.lowess(results.ypartial, results.feature, frac=1/3)\n",
    "    \n",
    "    ax.scatter(results.feature, results.ypartial + results.residual)\n",
    "    ax.plot(smoothed[:, 0], smoothed[:, 1], color='gray')\n",
    "    ax.plot(results.feature, results.ypartial, color='black')\n",
    "    ax.set_xlabel(feature)\n",
    "    ax.set_ylabel(f'Residual + {feature} contribution')\n",
    "    return ax\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "partialResidualPlot(result_poly, house_98105, 'AdjSalePrice', 'SqFtTotLiving', ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(result_poly.params[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:10.829628Z",
     "iopub.status.busy": "2022-04-26T19:56:10.829471Z",
     "iopub.status.idle": "2022-04-26T19:56:10.936519Z",
     "shell.execute_reply": "2022-04-26T19:56:10.935470Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:           AdjSalePrice   R-squared:                       0.814\n",
      "Model:                            OLS   Adj. R-squared:                  0.807\n",
      "Method:                 Least Squares   F-statistic:                     131.8\n",
      "Date:                Tue, 23 May 2023   Prob (F-statistic):          7.10e-104\n",
      "Time:                        22:26:13   Log-Likelihood:                -4211.4\n",
      "No. Observations:                 313   AIC:                             8445.\n",
      "Df Residuals:                     302   BIC:                             8486.\n",
      "Df Model:                          10                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "========================================================================================================\n",
      "                                           coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------------------\n",
      "Intercept                            -4.142e+05   1.43e+05     -2.899      0.004   -6.95e+05   -1.33e+05\n",
      "bs(SqFtTotLiving, df=6, degree=3)[0] -1.995e+05   1.86e+05     -1.076      0.283   -5.65e+05    1.66e+05\n",
      "bs(SqFtTotLiving, df=6, degree=3)[1] -1.206e+05   1.23e+05     -0.983      0.326   -3.62e+05    1.21e+05\n",
      "bs(SqFtTotLiving, df=6, degree=3)[2] -7.164e+04   1.36e+05     -0.525      0.600    -3.4e+05    1.97e+05\n",
      "bs(SqFtTotLiving, df=6, degree=3)[3]  1.957e+05   1.62e+05      1.212      0.227   -1.22e+05    5.14e+05\n",
      "bs(SqFtTotLiving, df=6, degree=3)[4]  8.452e+05   2.18e+05      3.878      0.000    4.16e+05    1.27e+06\n",
      "bs(SqFtTotLiving, df=6, degree=3)[5]  6.955e+05   2.14e+05      3.255      0.001    2.75e+05    1.12e+06\n",
      "SqFtLot                                 33.3258      5.454      6.110      0.000      22.592      44.059\n",
      "Bathrooms                            -4778.2080   1.94e+04     -0.246      0.806    -4.3e+04    3.34e+04\n",
      "Bedrooms                             -5778.7045   1.32e+04     -0.437      0.663   -3.18e+04    2.03e+04\n",
      "BldgGrade                             1.345e+05   1.52e+04      8.842      0.000    1.05e+05    1.64e+05\n",
      "==============================================================================\n",
      "Omnibus:                       58.816   Durbin-Watson:                   1.633\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              622.021\n",
      "Skew:                           0.330   Prob(JB):                    8.51e-136\n",
      "Kurtosis:                       9.874   Cond. No.                     1.97e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.97e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "formula = ('AdjSalePrice ~ bs(SqFtTotLiving, df=6, degree=3) + ' + \n",
    "           'SqFtLot + Bathrooms + Bedrooms + BldgGrade')\n",
    "model_spline = smf.ols(formula=formula, data=house_98105)\n",
    "result_spline = model_spline.fit()\n",
    "print(result_spline.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:10.940134Z",
     "iopub.status.busy": "2022-04-26T19:56:10.939905Z",
     "iopub.status.idle": "2022-04-26T19:56:11.136569Z",
     "shell.execute_reply": "2022-04-26T19:56:11.135874Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "partialResidualPlot(result_spline, house_98105, 'AdjSalePrice', 'SqFtTotLiving', ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generalized Additive Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:           AdjSalePrice   No. Observations:                  313\n",
      "Model:                         GLMGam   Df Residuals:                      295\n",
      "Model Family:                Gaussian   Df Model:                        17.00\n",
      "Link Function:               Identity   Scale:                      2.7471e+10\n",
      "Method:                         PIRLS   Log-Likelihood:                -4196.6\n",
      "Date:                Tue, 23 May 2023   Deviance:                   8.1039e+12\n",
      "Time:                        22:26:13   Pearson chi2:                 8.10e+12\n",
      "No. Iterations:                     3   Pseudo R-squ. (CS):             0.9901\n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept        -3.481e+05   1.18e+05     -2.962      0.003   -5.79e+05   -1.18e+05\n",
      "SqFtTotLiving      192.1472     50.663      3.793      0.000      92.849     291.446\n",
      "SqFtLot              6.9002      7.654      0.902      0.367      -8.101      21.902\n",
      "Bathrooms        -7836.2808   2.12e+04     -0.370      0.712   -4.94e+04    3.37e+04\n",
      "Bedrooms         -8297.4370   1.26e+04     -0.660      0.509   -3.29e+04    1.63e+04\n",
      "BldgGrade         1.014e+05    1.4e+04      7.249      0.000     7.4e+04    1.29e+05\n",
      "SqFtTotLiving_s0  1.465e+05   2.34e+05      0.626      0.531   -3.12e+05    6.05e+05\n",
      "SqFtTotLiving_s1 -6.174e+04   1.33e+05     -0.464      0.642   -3.22e+05    1.99e+05\n",
      "SqFtTotLiving_s2 -3.186e+04   1.26e+05     -0.253      0.800   -2.78e+05    2.15e+05\n",
      "SqFtTotLiving_s3 -5.403e+04   1.01e+05     -0.535      0.593   -2.52e+05    1.44e+05\n",
      "SqFtTotLiving_s4 -1.182e+05   1.01e+05     -1.167      0.243   -3.17e+05    8.03e+04\n",
      "SqFtTotLiving_s5 -1.295e+05   8.23e+04     -1.574      0.115   -2.91e+05    3.17e+04\n",
      "SqFtTotLiving_s6 -3.014e+04   1.27e+05     -0.237      0.813    -2.8e+05     2.2e+05\n",
      "SqFtTotLiving_s7  1.262e+05    1.8e+05      0.702      0.483   -2.26e+05    4.79e+05\n",
      "SqFtTotLiving_s8  5.325e+04   1.38e+05      0.386      0.700   -2.17e+05    3.24e+05\n",
      "SqFtLot_s0        5.775e+05   1.58e+05      3.651      0.000    2.68e+05    8.88e+05\n",
      "SqFtLot_s1       -2.771e+05   7.86e+04     -3.526      0.000   -4.31e+05   -1.23e+05\n",
      "Bathrooms_s0      3.723e+04   1.06e+05      0.351      0.726   -1.71e+05    2.45e+05\n",
      "Bathrooms_s1      5.303e+04   5.94e+04      0.892      0.372   -6.35e+04     1.7e+05\n",
      "Bedrooms_s0       2.298e+05   1.31e+05      1.751      0.080   -2.74e+04    4.87e+05\n",
      "Bedrooms_s1      -7.241e+04   6.81e+04     -1.063      0.288   -2.06e+05    6.11e+04\n",
      "BldgGrade_s0     -7.953e+05   2.03e+05     -3.917      0.000   -1.19e+06   -3.97e+05\n",
      "BldgGrade_s1      6.608e+05   1.14e+05      5.818      0.000    4.38e+05    8.83e+05\n",
      "====================================================================================\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.gam.api import GLMGam, BSplines\n",
    "\n",
    "predictors = ['SqFtTotLiving', 'SqFtLot', 'Bathrooms', 'Bedrooms', 'BldgGrade']\n",
    "outcome = 'AdjSalePrice'\n",
    "\n",
    "x_spline = house_98105[predictors]\n",
    "bs = BSplines(x_spline, df=[10] + [3] * 4, degree=[3] + [2] * 4)\n",
    "# penalization weight\n",
    "alpha = np.array([0] * 5)\n",
    "\n",
    "formula = ('AdjSalePrice ~ SqFtTotLiving + ' + \n",
    "           'SqFtLot + Bathrooms + Bedrooms + BldgGrade')\n",
    "\n",
    "gam_sm = GLMGam.from_formula(formula, data=house_98105, smoother=bs, alpha=alpha)\n",
    "res_sm = gam_sm.fit()\n",
    "print(res_sm.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4+msRajZvBmprtff17asNNZ06uayIDcaAQ0RE1IwUFwPffCNCzaZNQE2N9r7evbWhpksXlxXRLhhwiIiIPFxpqTbU/PQTcPWq9r5evbShpls315XR3hhwiIiIPFBZGfDttyLUbNwIVFdr7+vRQxtqevRwXRkdiQGHiIjIQ5SXA999J0LNDz/oh5pu3USoufNOoGdP15XRWRhwiIiImrCKCuD770Wo+f574MoV7X1dumhDTa9egCS5rpzOxoBDRETUxFRWihqa//5X1NhUVWnv69RJG2p6925eoUYXAw4RkRPklVQhp7AC8eEBiApWuro45OZkWSyLcPq08S0nR7/5qUMHYOJE0aemb9/mG2p0MeAQETnYmn25mLfuEFQyoJCAtPEJmDgw1tXFIheSZaCwUBtWsv6sQva5ChTlBuDcCSVOn9avlTEmPl5bU5OYyFBTHwMOEZED5ZVUacINAKhkYP66LAzvEsGanGZAloH8fOCPP4CDB8XXQ4eAU6dEMxMABPbORVjqIUgBgNwVKDqdgKqqWEgS0K4dEBenv8XHa79nqDGNAYeIyIFyCis04UatTpZxurCSAcfDXL0KHDmiDTLqUHPxovH9JQmI7lQF79GHgL+DiqQAIsZkYfMXEejXXQlfX+eV39Mw4BAROVB8eAAUEvRCjpckIS7c33WFokYrKNAPMX/8ARw9qr/UgZpCIUYz9ekjtt69ga5dgZgYIPOvCtz9sf7+KsiQAyvh68sA3BgKZzzJ0qVLERcXhxYtWiApKQl79+41u//atWvRrVs3tGjRAgkJCfjhhx/07pdlGQsXLkRUVBSUSiVSUlJw4sQJR74EIqIGiQpWIm18Arz+bkvwkiS8Mr4Xa2+aiKtXRYD5z3+Ap54CbrgBaNMGiIwEUlOBp58W9x06JMJNSAhw7bXAI48An3wC7Nsn5qY5ehRYvRqYNw+46SYx0snPTxuAdTEA24fDa3DWrFmD2bNnY9myZUhKSsLbb7+N1NRUZGdno3Xr1gb7p6enY9KkSUhLS8PNN9+MlStXYuzYsfj999/Rq1cvAMDixYuxZMkSfP7554iPj8eCBQuQmpqKI0eOoEWLFo5+SURENpk4MBbDu0TgdGEl4sL9GW7c1MWL2loZdc3MkSP6azWpSRLQubO2VkZdMxMTY1u/GHUAnr8uC3WyzABsR5Isy7Ll3RouKSkJAwcOxHvvvQcAUKlUiImJwSOPPIK5c+ca7D9x4kRUVFTgu+++09w2ePBg9O3bF8uWLYMsy4iOjsYTTzyBJ598EgBQUlKCNm3aYMWKFbjrrrsslqm0tBTBwcEoKSlBUFCQnV4pERE1BTU1QHa2fvPSH3+IzsDGBAXph5g+fcSkef52rGTJK6liALaCLedvh9bgXL16FZmZmZg3b57mNoVCgZSUFOzevdvoY3bv3o3Zs2fr3ZaamooNGzYAAHJycpCfn4+UlBTN/cHBwUhKSsLu3buNBpzq6mpU60wYUFpa2piXRURETYQsizCzbRuQkSECzeHD+otNqkmSaDpShxh1oGnf3vGjlaKClQw2dubQgFNYWIi6ujq0adNG7/Y2bdrg2LFjRh+Tn59vdP/8v6O1+qu5fepLS0vD888/36DXQERETcuffwJbt4pQs20bkJdnuE/Lltogo1srExjo/PKSYzSLUVTz5s3TqxUqLS1FTEyMC0tERET2UlUF7NgB/Pij2OqPOfHzA4YOBa65Rszy26ePqJVROGWYDbmKQwNOeHg4vLy8UFBQoHd7QUEBIiMjjT4mMjLS7P7qrwUFBYiKitLbp2/fvkaP6efnBz8/v4a+DCIiciOyLELMxo0i0Gzfrr/ApLc3MHgwMHIkcN114nuOP2l+HJpffX190b9/f2zZskVzm0qlwpYtW5CcnGz0McnJyXr7A8DmzZs1+8fHxyMyMlJvn9LSUmRkZJg8JhERNW3qFbNnzRL9ZLp2BR57TIScK1fEjL8zZgDr1gGXLgG//AK88AIwYgTDTXPl8Caq2bNnY+rUqRgwYAAGDRqEt99+GxUVFZg+fToAYMqUKWjbti3S0tIAAI899hiuvfZavPHGG7jpppuwevVq/Pbbb/joo48AAJIk4fHHH8dLL72Ezp07a4aJR0dHY+zYsY5+OURE5CR//gl8/bWopdm5U39xSR8f0eQ0ejQwZgzQsyeXLSB9Dg84EydOxMWLF7Fw4ULk5+ejb9++2Lhxo6aTcG5uLhQ6DaFDhgzBypUr8cwzz2D+/Pno3LkzNmzYoJkDBwCefvppVFRU4P7770dxcTGGDRuGjRs3cg4cIqIm7sgR4KuvRE3MgQP698XGijAzZoxoemrZ0iVFpCbC4fPguCPOg0NE5B5kGdi/XxtqdAfYKhRiVuCbbxahpls31tI0d24zDw4REVF9KhWwe7cINOvWAadPa+/z8QGuvx6YMAG49VYgPNxlxaQmjgGHiIgcrqZGDOVetw5Yv15/1mB/f1FDM368WKcpONh15STPwYBDREQOUV0NbN4sQs3XXwNFRdr7goKAW24RNTWpqfZd9oAIYMAhInJreSVVyCmsQHx4QJOYyr+8XAzd/uorMay7rEx7X3g4MHasqKkZNQrw9XVMGZrae0aOwYBDROSm1uzLxbx1h6CSAYUEpI1PwMSBsa4uloHiYuDbb0Wo+ekn/Un3oqNFoJkwARg2TEzC50hN5T0jx2PAISJyQ3klVZoTNQCoZGD+uiwM7xLhFrUSFy4AGzaI5qctW4DaWu19HTqIQDN+PDBokPOWRHD394yciwGHiMgN5RRWaE7UanWyjNOFlS47WZ89KzoIf/UVsGuXGA2l1rOnNtT07u2a4dzu+J6R6zDgEBG5ofjwACgk6J2wvSQJceHO7Y178qSopfnqK2DvXv37+vfXhpquXZ1aLKPc5T0j98CAQ0TkhqKClUgbn4D567JQJ8vwkiS8Mr6Xw2siZBnIytLOUXPwoPY+SRKrck+YAIwbJ1bkdieues/IPXEmY85kTERuLK+kCqcLKxEX7u+wE7UsA7/9pp1N+MQJ7X1eXmJZhPHjxQioyEiHFMGunPGekWtwJmMiIg8RFax0yEm6rg5IT9eGmrNntff5+QE33CBqam65BQgLs/vTO5Sj3jNqWhhwiIiaiepqYNs20VH466+BggLtfQEBYhbh8eOBG2/kQpbU9DHgNCFnz4r5JQICtJuj55Sg5qehk6Q1ZnI1TszmOKWlwI8/iiHdP/wgflYLCRHrPU2YINZ/UvKtJw/C06Obu3JFVCF/8AHw66+G9/v6asNOQoKoTr79diAiwvllpaavoZOkNWZyNU7MZn8FBcA334iami1bgKtXtfdFRQG33SY6CY8cKRa3JPJE7GTspp2MT5wAPvoIWL4cuHRJ3OblJdZrqajQn3+iPh8f8eH1wAPiA8wV81FQ05NXUoWhi7YaDLHdNXek2VqVhj6usY8lfSdPilqaDRtE3xrdT/YuXcRnwtixzp14j8je2MnYRcrKgLffBu69V0xP3hB79gALFgA//6y9rV074P77tceVZdGWXlkpwk5FhZgqfccOYO1aIDMT+O9/xZacDLzwglj3hUGHzGnoJGmNmVyNE7M1nCwD+/eLWpoNG8TQbl0DB4pAM24c0K2b/f//s1mR3B0Djh395z/AwoUiUIwbBzz8MDB8uHUfLJcvA/PmiVobWRaPGTMGePBB8VW3r40kAS1aiE13dMPgwcCcOcCBA+I4K1YAu3eLtvVrrhHlGjHCzi+aPEZDJ0lrzORqnJjNNrW1wC+/aGtqcnO193l5if/f48aJfjUxMfZ9bt1As/P4RTYrkttjE5Udm6h++gl4+WXxAaTWqxfw0EPAP/4BBAYaPkaWgS+/BJ54QqztAgBTpwLPPQfExTWuPHl5wKuvAsuWiRofQDRZvfKKCENE9a3Zl2swSZq1fXAa8rjGPrY5qKwENm8WNTXffgsUFWnv8/cHRo8WNTU33eS44dy6/aTU12u6Jw42K5Kz2HL+ZsBxQB+cgweBpUtFjU5lpbitZUsRXB5+WFQXA0B2NjBzphi2CQDdu4vOxNdea9/ynDsHpKUBH3+s7Wx4++3itk6d7Ptc1PQ1dJK0xkyu1twmZrPUvFNUBHz3nail2bgRqKrS3teqlRhMMG6cc0Y+GesnZcyqGYOR3LGVYwtDzR4DjgXO6mRcXAx8/jnw/vvA8ePa20eNEiOe3n9fBA6lUjRtzZ4tRkU5Sm4u8PzzouOyLIvOyDNnij4/4eGOe14i0jI1auzsWW3T044dYiI+tdhYbSfhYcOcOz1E+qlC3P1xhtl9WINDzsKAY4GzR1GpVGKo5tKloopZdwTUjTcC770HxMc7vBgahw6Jvjo//ih+DgoS/X8ee4zzYBA5krHaEAkSQn4ZiQPp+v/5EhK0oaZvX9cNEjBeZlEelQw2K5JTMeBY4Mph4mfOAB9+KIZxPvqo+ABz1QfXli3AU0+JkRiAGK310kvAPfeIDotEZF+/nijE5E8Na0PyVw7G1b9aYehQEWjGjgU6dnR68Uwy1k9qeJeIZtWsSO6BAceCpjAPjrOoVMDKlcC//60dkdGnj2jKuuUWzpdB1Fg1NcD27X8P595UBZ/xWyHp/r+SJTwaPxKTxynRpo3p47h6WHZz6ydF7okBxwIGHENXrgDvvitGgZWUiNt69QLmzwfuvJM1OkS2qKgQoyrXrxedhYuLtfe1GpSLwBFZgCRDIUlIs6J5h7M9EwkMOBYw4Jh26RLw5puiX5B6zZrOnYG5c0XTlSM7QTd1rr7CJtdSj3xatw7YtEl/5FPr1trlEa67Dii6Yn1tCGd7JtJiwLGAAcey4mIRct56SzvvRlSUWP7h/vvF902Jo8MHr7Cbp3PnxKin9etFM5TuyKe4OBFoxo8XM4o3tBbU1CgmDsum5ogBxwIGHOuVl4tO0W+8ISYOBMQQ1QkTxJw+w4a5/xIQjg4fvMJuXrKzRaBZvx7Yu1f/PvXIp3HjRF82e/zf4N8XkZYt5292ISWzAgPFLMunTwOrVolAU1sLrFkjlqHo0kX029GdMt6d5JVUacINIIa1zl+XhbySKvMPtIG59ZSo6ZNlMbXCwoVAz55ios5580S4kSRgyBDgtdfEArkHD4oO+vYc1h0VrETa+AR4/X1A9Sgmhhsi87gWlYdxVFOMry9w111iO3BAzOmzapVYwfiZZ8RkgaNGAXffLfoamJsy3pl9VZyxmCPXU/I8siwWr1y7VmzHjmnv8/ER/WjUaz5Zaq61x9/7xIGxBsOy2eeLyDw2UXlQE5Wz+4GUlwNffSUW9dy+XXu7t7c4Adxxhwg7ERGuK6Ozqve5nlLTJ8vA4cPAf/9rGGp8fcWaT3fcAdx8MxASYt0xHfX3zj5f1FyxD44FnhhwXN1On5MjFg1du1ZU0+tKTBS1O/2GVuHfGdaX0V5XqM4KH5wnxL1Y8/ejDjVr14pgYyzU3HmnmBPK1o8KR/2fdPX/dXIPzbUGz5bzN5uoPIQzmmLMiY8XTVXPPCPW3frqK+B//wN+/13MlLx/P+D33wpETjIs48erKtE7UonAQCAgQDQBbDuTi/f3HYIMcYX6xPAE3NorFn5+Yt0sWyYgVFfvZ56+DEhA//ahdn3talHBymb1QePOLNVw6NbUHD2qfZxuTc0ttwDBwQ0vg6P+T7r6/zq5HmvwrMOA4yHcqR9Ily6iE+a8eUBBAbB1K/Dzz8C2jADUqaA3i6uskvDC0/6oK9Mpd8sqtH3wkGY/lQws3paFxyZGoK5MiRYtgA4dxFT23bsDgweLYbiRkabLtPP4RX4gNBOmOpZHyhHY+r3SaKhJTdXW1DQm1Ohy1P9Jd/q/Ts5n6u97eJcIBtx6GHA8hHqkRf2mGFf/wbdpA0yaJDZAic+2J+CljVlQQYYkS+hQ1AtdkpSoqBB9eioqgNqwCv2p7AFIChnKiEqUlylx5Qpw5IjYftheBe/VFai9HID4NkoMHw7NFhcnRrIY+0CY99UhfiB4KFM1HKm3V6L6rPh9q0PNHXeIjsL2CjW6HPV/0l3/r5NzsAbPegw4HsTYSIv6XN1u+88RsRiTaKmMARi6yPAK9fjv/ogIEEPST50Cvtqfi42XDgESIKuA/J8S8NlnsfjsM/GYmBgRdGIHGn4gqAAs33Ua82/q7rgX6wKu/v26g5pLAZAA6P7KZZUEqcIft9zi2FBTnzX/J93puAD/htwda/Csx07GHtLJ2BpNqd3WUsdgYx0tFZBwM0bit51K7Nsn5usB1E1eWw1qhRQS8Ovc6zzmQ7wp/X7tSZZFH69168R29CgQ2DsXYalZkBQyIEsY27YXnp8a65RQ05Q117+hpqY5j9rkKCoLmmPAaYojL8yNSrI0fX1FBbBnD7Bzp9gOeR9BYP8cg/0jDg3Gdb1aISUFuPZa02tt5ZVU4bfTRZAkCf3bh7rde9YUf7+NoVIBu3drQ83p09r7fHxE89MNY6vQbUAlesVxVJs1mtvfUFPXXEdtchQVGWiK7bbmRiVZqqYNCBBD00eNEveduRCPEW/mGDRb7P/FH7/9ACxeLJosbr4ZmDIFSEnRjtRasy8Xc786pHmsBGDRBPe6sm2Kv19b1dQAO3aIEXobNgD5+dr7lEpgzBix7tNNN6nnqVH+vZE1msPfkCfhqE3LHLpUQ1FRESZPnoygoCCEhITg3nvvRXl5udn9H3nkEXTt2hVKpRKxsbF49NFHUVJSorefJEkG2+rVqx35UtxeXkkV0k8VmlyCQB0IdDXldltbp69v31qJRRP09589vBc+WaLE9OliteeSEjGXT2qqGPb+/PNA5tEqvXADiL4d89YdsutyD43lab9ftZISMd3AtGmiw/r11wPLlolwExQETJ4sAk9hofg6ebL1k/CRPk/9G6Lmy6FNVGPGjEFeXh4+/PBD1NTUYPr06Rg4cCBWrlxpdP+srCw8++yzmDZtGnr06IEzZ87gwQcfRO/evfG///1PW2hJwvLlyzF69GjNbSEhIWjRooVV5fK0Jipr2809sd3W1mpaU/vX1YkmrVWrRMgpLha3h448gqBBhk1bgPut5tyUf7/qjq1xrQJw6awSP/wA/PAD8Ouv+it0R0QAY8eKmprrrjPdpEgN05T/hqh5cIs+OEePHkWPHj2wb98+DBgwAACwceNG3Hjjjfjrr78QHR1t1XHWrl2Le+65BxUVFfD2Fi1qkiRh/fr1GDt2bIPK5kkBx9Z28+babmuLqiqxUvQHn1cht49h52RAv4OyO406aYq/30+25eLln0QtmSwDRRsTUH5Qe1Lt2lU0P40dKxZ79fJyWVGbhab4N0TNh1v0wdm9ezdCQkI04QYAUlJSoFAokJGRgXHjxll1HPWLUIcbtYcffhj33XcfOnTogAcffBDTp0+HZK/le5sQW9vNG9pu604ncUdTKsWioXFJFbj7Y8P7ZRnoVJyAnMNK7PDOxfz17jPqpCm0y1+8KDp+b98ObMuoQtkI7aSOkgSEpWYhOS4Ct92gxJgxYlJHcp6m8DdEZA2HBZz8/Hy0bt1a/8m8vREWFoZ83d6BZhQWFuLFF1/E/fffr3f7Cy+8gOuuuw7+/v7YtGkTHnroIZSXl+PRRx81epzq6mpUV1drfi4tLbXx1bgvZ8yJ0FyHjhp7byED+V8MQW5+KLaurEK7mWIeHsD1M4q6awg9d06MeNqxQ4SarCztfX6xFYg0Mqnj829WIrmj+7wGImp6bA44c+fOxauvvmp2n6O686A3UGlpKW666Sb06NEDzz33nN59CxYs0HyfmJiIiooKvPbaayYDTlpaGp5//vlGl8kdOXpWU0dPC+6uJ2XA+Hv78vheiB8fig8+AL7eU6EJN2p1sozP/luJaTcq0bat88rqLiG0slKsP7ZnD5CRIb7+9Zfhfr16iWH5fYcE4JVDnLSMiOzP5j44Fy9exKVLl8zu06FDB/znP//BE088gcuXL2tur62tRYsWLbB27VqzTVRlZWVITU2Fv78/vvvuO4udh7///nvcfPPNuHLlCvz8/AzuN1aDExMT4xF9cNQc1W5uab6ZxnCXk7Ilpt7bnIIqXPfWVoOh5+eWjURdmRKxscDQocCQIWLr3RvwdkCdaUPmL7FHsCwtBf74Q39B1cOH9TsFA2K4fUKCmFV6xAjgmmtEZ2E1dmwlIms5tA9OREQEInQ/nUxITk5GcXExMjMz0b9/fwDA1q1boVKpkJSUZPJxpaWlSE1NhZ+fH7755hurRkYdOHAAoaGhRsMNAPj5+Zm8z1NY225u64mtIU1g1jxHU1owztR7G99GDD1Xn5wVkJAo90JYRyUOHhRLSuTmipFZgJibp29fsSUmiq1nT6Cxf5q29sOyNVjW1YmlMQ4fFtuhQyLUnDxpfP/ISLH4aVKSWAi1f38gMNB0+R257IC7cueaS3INlUpskqS/UcM5rA9O9+7dMXr0aMyYMQPLli1DTU0NZs2ahbvuukszgurcuXMYNWoUvvjiCwwaNAilpaW44YYbUFlZif/85z8oLS3V9JeJiIiAl5cXvv32WxQUFGDw4MFo0aIFNm/ejFdeeQVPPvmko15Kk2Lug7MhNSa2NoFZ+xymTso/HynAP5LjbHrNrmTq5FxWBuzdC6Sni233bjGny6+/ik3Nxwfo0UMbeLp3B7p1A9q21U40aIktIdRcsAxroURODnD8uDbMZGUBx44BOhWgetq1A/r105Y/MVGsAWbrB7O9OrYa+/t3tzDRVGouyXZXrohO9BcuaL8a+/7iRdGce/WqdlMvLVNf/cCjUIivXl5AeLj4P9i2rfar7vfR0Y2/gGrKHDoPTlFREWbNmoVvv/0WCoUCEyZMwJIlSxD49+Xc6dOnER8fj23btmHEiBHYvn07Ro4cafRYOTk5iIuLw8aNGzFv3jycPHkSsiyjU6dOmDlzJmbMmAGFlWcEdx8mbuoD2dIHtbkPTkvNGOpjB/h6oeJqndHntnSFbUtTyR9nL+O2pelGjzOhX1u8cWdfS29Tk6JSiaCgbspRbzotuHr8/YEuXcQQ6a5dxUiimBjxwRURIWZd1v1zt9TMU1cHXLoE/HywEPN/NmxylLcMxtnMVjD1aaBUivDVs6foP6MOM+HhjXlX7MvY3z8AtwoTXA6haampEZNIWgos6u/LylxdYkMREYbBp/73wcFNp7bILebBcWeuDjjW1rJIAO5PTEBSRCx+/jMXq06KuUKMfVBb+uA01ZfmvUmJqLhaq3dVDxPPYYkt/XVM7av29cND0Ccm1OrntoU7XNHnlVQh52IFfKoDcO6EEgcOAAcOANnZounH1NWcmpcXEBYGtGolwpCXFwBlFeTASvjV+MOrWonKSnGVeOmS+BBWqYwvPKrbbygwEOjUSQQZ3S0uzrb5Z5z9HhtffBWAkZotV4YJR/ZpI8vq6oCiItMBpf7Ppi5AGkOSxP/bwEAxUaV68/MTFy2yrL+pVMZvq60FCgpEAGusgADLIahNG/eYg8ot5sEh44xdZd45IBY7dgDvL69CRuQhTZKWASzLzMJz/9cSkf/QzhWikoE5/8vCL2sjMCpZieRkIKfCfD+M+PAASH8fU9esVfuN3t6QPjG2NJUYHYKt47fTlx0ScOoHyBnXxGP6sHinnvCM/Q08N1YbJGtqgJwcEXbU25kzwNmzYkRSebn4oFZXdWtZXnspxFcJv4MJuNonC5BkSJAwtl0v3LlRiU6dxNWe7pWcOqj4l1sfVFzRBGOsyVMFGPxhu3ptJWdM69DclZeLZtYTJ0TfMd3t/HkRDuwtNFT832ndWmzq743d1qqVfYPClSvidZ07Jz4fzp0z/P78efMXTRUV4j07ftz0Pl5eQFSU6QDUrp1oElO6UUUkA44TGev/MPerLLw8KwKH9irFnCCT9B8jKWT4tb1sOJuuJOOTVZVY+pr4a2rfPQC4BXrDlut/cJqqqjN1u60nA1v666j3rb/Ok9qAOPuHm/rvvwzgo19y8MmuHKc1XVjTudrHRzRPdekC3HKL4TGqq0WtzKVL4urtyhUReGprxVf1KCZ/f/FhExYmrr4iIsSxgVjklVju1NuQoOKqzuPGgoOpGhxXhglHT+vQXMgykJcnmn3rb2fPNv74LVsaDyfGvg8Pd+2SIS1aiCZscxNiqlSiRspUAFJ/b2apSNTVif2MTfugKyxMP/i8+64ooysw4DiRsatMGTKO51UCUKL2cgBkFfTCjCRLmJQSik2AwXDk2mLtB/WZo0oE+iQgLDULkkJcmT8zWvvBmXnG9rpWhQSbTwbWjIhR1wp0i2yJJZP64j97cpGRU6S5f0K/tg6pvTH2/gPOHcFljxWb/fzElZKVq50YZalTb0ODiqtWpDYVHAAYDROubKZsjqPGGqqmRjTZqsPL0aPa723t7xIeDrRvrw37pgJLRIR71ULYg0IhRjdGRgI6iwsYKC21HIIuXDD/XEVFYjt4UHxWffSRfV+LLRhwnMjYVaY6qAwcCPzrX0qo4hKw8BudD+QJvTBxYCjW7NP/8J6b2guthyqxe7cYpbNvH1B+MBZVORHwDqlEbbE/HnlPiSMzgS6jc/HK5kM2l/e+YR0a9OFr7uSpWyugppCAh0d0RFiALwbEhTqs7425ZjFnNV00lSaKhgYVV74+U8Gh/m3uMIqJyyEYunBBnBR1t8OHxQgja4WGakcidu4s+pN17ChqN4KDHVd2TxEUJEZ19uhhep+rV7VNYuaaxWpqRA2OKzsvM+A4UUGOEld+SYDPEFHLIqskdLjUC6t/UmLwYPUfQiyu62H4IW3qw/u228SxKyqADRuATz9VYts2cV8FgLc+rEJb6ZDRBSPNUQCYPizO4PbGXPnWrxVQU8nAsh1/Orzzp/oqf95Xh1C/Gd6ZIeOugTFYve8sVDLctomioUHF1U0wxoKD7m0NrZlyh47pnuLqVVETow4xf/whvhYUWPd4SRKd3rt109+6dxe1NE1lNFBT5esr3v+4ONP7qFSi+bykxFmlMo4Bx0nS04EbbwRKSmLhdTACXftXYvkSfwxKsH5BTHNXfQEBwOTJYjtyBHjvPWD5ckAOrbA53EgAZo7oiJzCCs3zAo3vPGqqiQhwXg2KOigu33Uan+z6s9Ehw5YT35p9uQZ9jp4e3dUt50BpTFBx5yaYhtRMuUONT1NVVSXCy++/A5mZ4mtWlri6t0ShEP3QEhLESD51kOncWfQvI/elUGib/VyJw8TtPEzc2Alv0yZg3DgxZBcQs7x+/72oTrX02EaVJQ94fnEVfvTZanPIUVN/oA/vEtHo+TuMDeVt6LHswZblLYz9boyd+IZ3iTA5h9GQtK0GHaoVEvDr3OvcKgToctQSIK5i6zw0f5y9jLHvp+vND+TqoebuqrJSzO2kDjKZmaKmpv7SHca0agX06SOWM1FvPXp4Xl8YajwOE3cRYyc879xYTJqkvWJJSQHWrzecut4RV4lRUcCyt5T46OcEpG3OgizJkGXbqnDVVfjP3drD6JXv9wfzcFPvKKs+7OvXCqi5qvOntf0gTAUZwxFxYoi/sd9hTmGF0dFiKhkuHbZsiaf1FbGlZspYjRvg+qHm7qCuToSXvXu128GDlsOMQiFqYfr21Q80UVFsWiL7Yw2OnWpwjF0ZSpBw7oORqCkVH4TjxwMrVwJFV/RP4s6Y3TSvpAr7T1Ti3S+LccQn++8+QADssN6JrYFMXSvg76tA5VWV0c6fEoC5Y7rhgWs7Nq5wjWTqd/POpL6YtXK/2cfWnym6Kdbg2Iu79WGxVDNlrrZRApA+z/N/Z2qyLDqPZmRow8xvv4l+f+Z4e4umpX79tFufPqI5naihWIPjAqaGgCuCK4FSJaZNAz7+GPhqv2FtQEyYv8OH1kYFKxE1QIkbB7TCzt+isfC1Suz60R9eAVcQOSW9USHH1mHWxmoFjM1Rk/bjMUACHhjeUW8/e50orTmWqT4b+Pv3Z6pPkXo/9e8wKlgszKlbIyD9/fs3N5TenoHAmSFD97l2Hr/odn1YLNVMmesvBg+vaSguFqMydWtn8vPNP0aSRJgZNAgYOFAssJqQ4Lr5T4gABhy7MTcE/NFHgbfeAgrKjI/gWPdQslOH1g4foMT2NUps3gz8699AmQyTH9oKwGDEkTGNDWSmTiiv/ngMt/aJtvvwXmuPZWo0Uf+4UL2mDgVEKNN9CfV/h+rOt5mnL0OSgH7tQ21eU8wa1vYXclTIqF8TB2jfF1Nh2N1qeMxNKSC7ebOiLWQZOH1aLAC7a5f4evgwTK5JphYbK8KMeuvXT0yOR+ROGHDsJCpYiZfHJWD+V3/3dVFJKPqpF56ZrcSzz4orHFO1AZVXVS4ZWnv99cCyuArc86nhfVV7uuCeMWGYdIcCd3yUbnK2Y7XGBrIAX+Nzl6v7qACw2wy5tgwVNtdno/5ooZ3HL1r8HUYFK3FzH/tPsKdmbX8hR01saKwmrr76YdgdRym5y5QC9lZbK9Y80w00eXnmHxMSoh9mBg4UE8YRuTsGHDuprQU2fRCLs19pJ9p7/QUlHn9cu4+5uUWSO7ZyydDajq2NX6m2GHQcH61MwDefxOLOJxPw3xz9ppXxiW2xYf/5RgeyvJIqfLYrB5/uyjF6vwLApYpqXKqobnQzXl5JFX47XYQTF8ptOpa5Yc+6TR32GB7dmJmATYWjdyb1ddrswmabdv6mGxBctbSDNew9pYArlJQAe/ZoA01GhnY0pzFeXqIDcHIykJQktk6d2AGYbOcOtbIMOHZSWAhs3QrUlSkhVyjxySfA9On6+1gaweGKESs7j180Wh0tKYCw1CzkLIvA4gdjkTwqAhMfuoxOnbRNK0+mdrXYUfO300WQJAn9jTTHmBqloikDRA3ArJViQdD6i4LaciVt6bkAwN/X9Fj6+pPF1f+Pq3tbY1aFbsxMwLb0F3JULYSx8kuAZnRZ/b/5zDOXXbK0Q32mPoyjgpWYf1N3TB8W5/bD5WUZyM0VYUa9HTxovrmpZUsRZoYOFVtSkuEITyJbuUutLAOOnURGAps3A6NGAW+/DUyYYHw/d5oE7Y+zlzF3nZmAoZDhHVKJujIldm8R25gxwLPPAlFJlpdk0OtQC2DRBO0fufrK3dRzj0uMxoYD5zUfzupuQuqTpwJikjxjnZWNhQ9L4QYAKq9a7m1k7D8uALv9Z27MBHvW9hdyZC2EqfIb+5tX/43U5+wmIGs+jN1xuHxtrQgwuoHG0kKIMTHAsGHaQJOQYN+VrYncqVaWw8TtPNHflStNY+SANTUaXpKEhYkj8fIzSpw4oX/fDTcACxeKD8n6rBkS/cr3R/DRL8abpcxJig/D3pwiyDA8GdU/Uc0Z3Q0J7YJRVHHVpiHdphgbOmxqxerGDvFv6AR7a/blGoQL3VBpyzEbuyxHQ4ZhO/tqzxlTNNhLWZm2uenXX8X35lZ/VijEHDO6gSYmxnnlpeYp/VQh7v44w+D2VTMGN6p2W43DxF2oKYQbS7UnAHROjkrcPQ747DPglVdEFTggZmfetAkYMgR44AHg9tu106dbmtQOAD5uQLgBoLfquEoG5q47hOFdIgAYdkJO+/EYAMujek3VBgHak3yArxf2ni4yXEcLMOhJa48mlobWGFjbX8iSxlYxN3QY9pK7EnFzn0Ysk24jV61+bo2//tLvDPzHH2KNH1MCAoDBg0WQGTZMNDfZ+fqNyCJ3WlCYAacZMnVyUdd69G4Xondy9PERIWb6dOCLL4CXXxZDSwGxxlZ6OvDoo8A994itfdcAg/4yartOXkTWeR+j9w2OD8MenQBjDVkGfj9zGaEBviY7t+qOgtftKD2mZyR+PJwPlQy8uvEYQvx99E7ixlY+t4arR9mof2/11xKrz1QNjSOrmHUDo6nmNGdylw/jujqxRpNuoFFfTJgSHa2tnRk2TNTWePMTnVysMc3s9sb/Ds2QsQ91BYD1Dw1BnxjTJxhfX+C++4CpU4EvvwTeeEN8KANitMbSpWKLjFSi7/gEHG1p2L9i6bZTRo+tADDvxm4Y9366zYFCls3PWwIYhq2Hru2ID3ac0vTxqX8SN7XyuTWeHmO8Nqg+R40ysKb2xdw+DanVsOa11H/OcXYaidcYrvowrqgQI5rUgWbPHqC01PT+kiT6y6ibmoYOBdq35+gmck/u0teUAaeZqH8CMvahbi7c6PLxAaZNE0EnPR345BNgzRqxcjAgZj3d+H4s/Lt6I2Ks+b4vaje1SUDGj6EYHtQNO0qO/V3rIqGTdzRO1p6HrI4outUxfys6EYp8lRLzrxdrbtVZ0a3sg+2nDOY30T2JWxru/Nh1nbBk60mjNVG924ZYfH5HjTKwpvbF0j621mpY81qMPeeG/eex7qFkveU6XMGeH8amgt7589q+M7t2iblozK3bpFSKJiZ1Dc3gwWI+GqKmwh065jPgNAOmTkCN/VCXJO3V5NtvA199BXz9teibc+UKUFuitGpxT1kFrFgBALkISz0GSSFuu7yjK37e2xFeLbvCO6QSvlHFCL32mOZ4sgwUbUzA1FfVZY+Ff3gEYobk4Ur3o2afU/V3+euvEq0+iceHm25m85IkdI5safQ+hQS9OV6Mnew0o9cc0ARkTe2LpX1sqdWwtjnL3CSX9uh42Fj2+DCuP4Pz6FYJKD8Yi127tE26pkRGapuahg4Vc9H4+DSqOETNHgOOh7N0ArJXwg4OBv75T7FVVooq9w3pdfjGzCgPNTHnziHN9+qvIddmo+JoNOrKRBnb3LVHcz8AQAZU1d7walml2ae6GsjN9kNEVxjsq1vz4yVJeHpMVyz+MdumpgmFBLwyvhf6tw812iQ2Z0w3s8tKOHqFamtqX6zZx9oAbG1zlrv0dXGEykpg484qzN2u/b3KAH64mIVzX0do/jZ19eypH2ji49ncRGRvDDgezlGjRCxNdnfddUp07x+A7xaZX5BSTTIyx56kkLH4/Ur0CFdiZ14BVmYbPiZirJgEsHt5AoqKgPyYQ4AkaoDUtUeySkLF4WgE9Dz/9yrqEuTfe+FoVSwWXR+NyE6V6BQpTrTppwoRHx5gciSY7igf3VoOCcBDIzrigeEdjYbKeV8dwtU6FZ79+rDJWiFLNT/WsKb2pf4+CgATB7ZD5pnL6N9e2ynZmgBsbXBxp46HjXX2rGia3b1bfN2/H/CKrkDkJP391PNI+dQoMWiQtrYzORkIC3NN2YmaEwYcD+eIK2drJ7sDLC/aZ44EYNIt/th5PBcrtx82uZ8M4GjgIUBnBlZ1YIqvi0H53s44kK5E8S9dNcto1JUp8c4W4J13lAgKUmLAnbn4s9Uhzfw6qT0NF9uR/v4nr6RKsxZVcVUNFv14DLIMfLDjFGJb+RtdHV4FYMEG469BXStkrubHFtbUvuguQ/DxL39i5d6zWLn3rMGEjJbYElzs3fHQliDY0NBYUyP6y6SnA9syqpB5vAJ5xwMMamXkywGQVfpBXYKErz73x/VDRQd9InIuTvTXDCaKsGeHVmsnuzN2my7d6ftNkQBseHhIg0ZWacrx9+SCwT5K7NsnOnj+/DPwyy/aTp5eLavQ9sGtRmuRTB1TvYilsUni1j2UbHWZFQDWPyxGrzl70jlrJmS05VjOHDFhy9+0LftevKitmUlPB/btE/3JAnvnIiz1kKZ/WNFPon8NAHTrJmpm/Hvl4ruCLKiMTLJIRPbBif5I74oVgF6H1lMXyjW1ELYy1uRlbLI7Y7fpkv/+5+6kGKzMOGtyn32nDdcqsoV6csHkjkpcey1w7bXAv/8NFBcDP/0EfPst8NP+CqvDjfqYc9cdwruTEq1aHd6U+qPXnD3pnKUJGW15TmeOmLBlnh5z+7YOVOLIEW2YSU8HTp40fD6vllWacAOIWprwMVn48PkIjB6u1GluisW8EtcPjSUigQHHA9UfzQHoZ42PfsnBJ7tyGlSTY2oOHWtqcOqXRQawykS4UR9jYJzxzry2OHiu2GCkTkgIMHGi2N7fWoLFm/QfY2n0lywDZy5VmByJpV4dPvP0ZTy6er/Be/Pu3YmaRUvVnN0R19RIMd2RYGrusDKwmi1B0NS+d0yvxKEtSrNzzwCi82+PkRXIqheAZciIT6hEWJj+87nD0FgiEmy4bqWmoP4VqwzjFSnqK9m8kiqbjq/uc+H199nfS5KQNiHBqtsmDYoxKIu53CIDOJZfhrTxCY36Q138Y7be68wrqUL6qUL8cfYyvv3jHF7ffMzgMfWDizGv/XTcINzUXx3+5j7RRt+bm3pHG5wIjb23usdTl9vW35kpUcFKLJqQoDetkPR3E45u2dbsy8XQRVtx98cZGLpoK9bsszDFroOpg6Cu+kGwrAzYvh3YuDbA4I9MVknY87O/Qbjx9RVLjzz5pJjy4Px54M8/gY/ftPx8ROR+2AfHw/rgmFrozJSGLoCWV1KFzNOXAQno/3dNhLF+GOrbDp4rxqIfjhkEGmM1TLrUw7lf/fGYpkaqIX+w6tfZ0OUXLNHtS2OMLX1UjO3rqIkB1c+XefoyJAkGtUruuhil7qKiCknCA/17IehiLPbuFf1mjhzRBlTRfyZLM4Ku6KdeKD8Yq5l7JjlZBJt+/QA/P8vPx/41RK7DPjjNmLFmDlMhojFXoTuPXzR6wjVWKwEAkz/ZYzKYjO/XFut/P2cwszAgmhPUo5SMvQZrqF9nY5ZfsEQFYGtGFXq3CzXatGVL00X9fR25NpT6+W7u07h5bpylrAw4eBAoPBCL5EsROJhTiZP7/TF3kemylB+MhaIgAt0HVWJgd3+MWCiGbbdrZ/3cM+4y9by9uFOTI5GjMOB4GFPDdtVDgj/Z9SdUsmHzhy1sPeGaW/ZAhpiyf/3DQ3C2qMpofxVjj7W2Jkd3CHb6qUKrw40EEbzUayVZ83xv792P95ZX4Z4BHXH77UCvXvaZvM3RIcPcyc7afkH2PmHKMpCXJ4Zo798vvh44UL8TsPLvTZ+3t1h4ctAg7datmxJeXo0rl6f0r3FkbSCRO2HA8UCmrjbn39Qd04fFNfoq1NoTrrmVo+s/tvKqCjf3iUbF1Vq9cPb06K54deMxgxNs/VmI7x0Wh49+yTE4tu7EfOaWXzBmUHwYnkztitOFlfD3VWDs0nSzj5UUQG2vY3hrI/DCCx3RuTMwbhwwerRoAjHV/GGJIzsfWzrZqQPzvK8OQQUROOsH48aeMKurgePHxcKtumHm4kXLj5UkoHNnYMAAbZjp21es5USGHF0bSOROGHA8lKmrTXtchVpzwjW2crSpZijdUTv1w9nO4xf1OvJKf9fITBwYi1v7RGv2A4BPduUYhKhzOh1ydx6/aHW4kSE++HfNHYnkjq2QfqrQqsdKEhB67TFU54bhxIlQLF4MLF4M+PuLIeo33ABcfz3QvTugsLLntKNmAbbpZKdOhlLDj1FYKIKMesvOFn1lTpwwv/CkWosWomamb1+xJSaKFbYDAhrw4pspd2tyJHIkBhw3UlkprvK9vFxdEvPMnXDzSqrw2+kioytHfzy1P+77ItNgdJJ6/Sbd46uPNW+d/rpNkgwM7xKht5/mOKO7Ie1H/RFRi3/Mxq1/1+DMW3fIptep+8F/6K8Sqx8nKYCoKeko+ikBZX+ImozKSuDHH8UGiGHqSUlilejBg8XJunVr001ajugDYs3JzlKAMXWM5WsrIRco9QJNUZH1ZYuIEO+JOsz07Stqarz5idUonrwmGFF9/LhwocpK7cy6W7aI6nkfH6BTJ3GlP368aNpwx8Bj7IRrboRSnSzD39cHi+qtgTRnTDc8MLyj0ecwNamgqavNS+XVRp/3dGElZMg2dy7W7Zz86kbDoeRmSWIyuFcfj0DGdiU2bRJ9StTUEw3+9JOYSM47tAIt5QB0j1OiZ0+gRw8xQ25sLBATI2qA7N0HxJqTnakA88H/VUK6qMTxvwKAcOjV7MgqCQtn+6OuzHIZ/PzE6+zZU2zqMBMV5XmLT7pDx15PWhOMyBIGHBc4fBh45BHg11+Bq1f177t6VVTbHzkCvP22uKofO1b05bjuOvdZ06b+h7WlEUoKAJcqqjG8SwR2zR1pVU2ELVebeSVV+NhIHxx189c3B87b+hIxNjHa5s7JulSyjL7XVGLmNCVkWfzeN28W87PsOViFElUFfCNLEHrtMc0SAPt/SsAvywz7r4SHi6DTrh3QqpWoAQoNFV/V3wcHi78PHx9R0+HtLb5XqcTfVU2N2NTfF5QBw1vHY0dBzt+tTxIS63rh2aeUuHwZKCgACkoDIN+gv8aSrJLwynx1gFEisHeCwTDs+ms1xcQAXboAXbuKr126AKHRVahRVqBTG88fyeNOHXs9bUQYkSkMOE4my8C0acBvv4mfY2KAlBRg1Chg5Ehx4snMBL7+GvjmG+DCBeCjj8QWHAzccQcwc6aYs8NVjH1YG1tgUk3dfWPWyv02fbjbcrVpatmB+4Z1AACjNTA3JkTip6wCTW1S/UkRN+w/jydTuxoNWtbQ7VskSWJUVa9eQPSwXBxadwjKeseTFEBYahaqciIMAkJhodj277etDKbUX1updF88yjLjcbqs/nurRKBsPsCUH4yF18UIRHepRGyoPzoOU6L9ZDELcNeuokbSv14mXbMvFw+vdo8TvqO5Y8deTxkRRmQOA46TpaeLcNOihZiQrGdPw6r49u1F81RNjbjaX7cOWL9eXFF/8onYBg0SQWfiROeOGDH1Yb3uoWSjSzi8MLYnFn59uMEf7tZebZpaQmL6sDiTw9T/MTgOC27ugdOFlbhUUY1ZK/XTg7p5K7ljK6vWlqrvvmEdDEaV1e+fVJ+kkLHsP6L/yp9/Arm52i2/pApScAVqLxuuZm0LY2srBQ08jbLMeIN9W7YEWlfEotXeCARGV6JdkD9ib1WizQzxd6regoOND9muz1QfLVef8C1pTPMSO/YSuQYDjpO9/bb4es894mreHB8fMeLm+uuB994TK2B/+KGYRn7vXrHNni1qhB58UFT7O5qpD+v6C0yqa1uM1ezY+uFuzdWmpdoeU01duh2azTWHqYOWqbWljK3FNX1YnOZna2dQVkjATdf6IypY/3a99cUk4L7eCUgMjsXly6I/T3ExUFIimp9qa4Gy2iqUyhXwuRIAf0kJHx9t81WxbwW21xvBJSlkvPZBJQbEKhEcLGoLW7fWDc/WBRhzLPXRcuYJ35bA0tjmJXbsJXINhwacoqIiPPLII/j222+hUCgwYcIEvPPOOwgMDDT5mBEjRmDHjh16tz3wwANYtmyZ5ufc3FzMnDkT27ZtQ2BgIKZOnYq0tDR4u/kQizNnRG0MADz6qG2P9fICRowQW0EB8NlnIuycOQO89ZbYRo0StTq33ea40SbmPqzVC0zq1rZYCg72ZKq2x5qmLmubw8ICfTFnTDe9OXheGS+SqqnH2jKDcv1aH2OPl2Xgs4NZ2DXXeI2HpRNyXkkAhi4y/J3cdbNhsKqvoTUZlt4DZ57wbQks9mheYsdeItdwaCKYPHky8vLysHnzZtTU1GD69Om4//77sXLlSrOPmzFjBl544QXNz/46Dfh1dXW46aabEBkZifT0dOTl5WHKlCnw8fHBK6+84rDXYg9Ll4oOn6NGifk7GqpNG2DePODpp8UonA8+AL7/XozE2rJFNBn861/AvfcCZrJkg1j6sK5f2+LsD3dTtT3mmrrUJ21zHaDrnxTnjO6G3u1C9PYzdXxzMznrql/rY+7xpmo8rDkhN/R30piaDHPvgXpCx5zCCk35bGFL6LLHLNwNqW2q//cHiHXjuFQCkeM4LOAcPXoUGzduxL59+zBgwAAAwLvvvosbb7wRr7/+OqKjo00+1t/fH5GRkUbv27RpE44cOYKff/4Zbdq0Qd++ffHiiy9izpw5eO655+DrLsOM6ikvBz7+WHz/+OP2OaaXF3DjjWI7c0Yc/6OPxPePPw48/zzw8MNixFbr1vZ5TsD2URjuMmrDWPjRa/oBMHdMNzxwrf6wdWMnxcUbsw0WnDQVrkz1D5qUFItVe3MtLp1hSxOHtSdkW38nja3JMPUevHt3Iv4qrtIspmprcLI1dNkaWOzZvKT++3CnEVW2shQm3WEoPJGalXOp2m737t0ICQnRhBsASElJgUKhQEaG+dWuv/zyS4SHh6NXr16YN28eKisr9Y6bkJCANm3aaG5LTU1FaWkpDh8+bPR41dXVKC0t1duc7YsvRD+JTp1EILG39u2Bl14S4WbZMjEp2uXL4rbYWNFH59Qp+z1fVLASyR1b2VRNr161PP1UIfJ0Zhh2FYOmHwBpPx7Dhzv13yhzJ0XdY5l6XeoaE6+/e5MrANx3TTzuHNAO79zVF0vvTsSuuSNNnuTqP96aMKTL1AnZlt+hNe+BOVHBSoxLbKt327h+bdGvfagm3ADa4GTN34ep0GXusQG+Xgad+s0FFlvee2s0pMzuYs2+XAxdtBV3f5yBoYu2Ys2+XJvuJ3I2h9Xg5Ofno3W9agNvb2+EhYUhPz/f5OPuvvtutG/fHtHR0Th48CDmzJmD7OxsrPu780p+fr5euAGg+dnUcdPS0vD888835uU0ikoFLFkivn/0Ueun6G8IpRJ44AHgvvvEUPPFi4GMDNFf59NPRbPVwoWAmQo0h3G3K1dTzSav/ngMt/aJ1pzELF3FW/O61DUm6gVPP/olR7N2ljXvhbU1LtY0PzXkKruxNRl5JVVYv/+c3m0b9p/HyG6tG9wEZGttjPr3JNd7DZYCiz1rIJvqiCpLNXjuOBSeyOZT7dy5cyFJktnt2DEbZ33Vcf/99yM1NRUJCQmYPHkyvvjiC6xfvx6nGlH9MG/ePJSUlGi2s2fPNvhYDfHTT2LdnaAgMeLJGby8xFDz3buBnTuBMWPE6JoPPwQ6dhT9dy5datxzmKu1MLavo65cdcthS5kCfI1PEa2SoVczYe4q3tbXpV7Nvf7zWfNeGKtxMfZ6Jw6Mxa65I7FqxmCDmqGGXmU3tCZDXb7MM5eNntjxdyjUZW1wsqW2ylgnZwWAdQ8l1+uAbfzvx9YaS3uU2Z1YqsFrbA1fc2PL5xQ1nM01OE888QSmWThLd+jQAZGRkbhw4YLe7bW1tSgqKjLZv8aYpKQkAMDJkyfRsWNHREZGYu/evXr7FBQUAIDJ4/r5+cGvoUs524F6aPh994l5RZxJkoBrrhHbL78A8+eL5SFee03013nuOdFPx8fHtuM6uu9DQ8qhPm/IsK5MFVeNr/CoO0GfmqmreFtel7mOtuY6DZuqbTH3OzDWH6ixV9nWdNTWLaex3019MWEN74Ru60SQxpb9qLyqXf7VGTWMTXVElaUaPA6Ft5671WR7MpsDTkREBCIiIizul5ycjOLiYmRmZqJ///4AgK1bt0KlUmlCizUOHDgAAIiKitIc9+WXX8aFCxc0TWCbN29GUFAQevToYeOrcbwjR4BNm0Sz1KxZri3LNdeI2pwffxRB548/xGirjz8WTWijRll3HFMnym6RLVFxtc7oydgRH4DG+tCoWXPyNjVDcf3FP9WMhQZbXpe5GZGNPcbcB2FDwoo9Qqaljtrqcg7vEmHyd6Nr3PvpSBufYPXyHfU1ZiJI3ffcmU0s7tLp3hbWjJ5sisHN2diU51wO6w3SvXt3jB49GjNmzMDevXvx66+/YtasWbjrrrs0I6jOnTuHbt26aWpkTp06hRdffBGZmZk4ffo0vvnmG0yZMgXDhw9H7969AQA33HADevTogX/84x/4448/8NNPP+GZZ57Bww8/7NJaGlPUfW9uu01MXe9qkiQ6OWdmimATHi5CWEoKcPvtopOyJaZOlGPfTzfZ9GHvzpqmylG/TOaqyI11/p1nZvFPa45h7nXV31fNVD8Zc01fDWkScETziKlyGmuSMka9P4AGNwFZ03xk6ffk7CYWezV5OZO5pk9r7ic25TmbQ+fB+fLLLzFr1iyMGjVKM9HfEvUZH0BNTQ2ys7M1o6R8fX3x888/4+2330ZFRQViYmIwYcIEPPPMM5rHeHl54bvvvsPMmTORnJyMgIAATJ06VW/eHHdx6ZIYPQXYb2i4vXh5iSazCRNEM9XSpWKG5B9/FD8//rjpZitTNRGyhasSe1+5WlojypqTtz3KZMsxdPf191XgbFEVIAH924fq7WeptqUhNWKOuMo2VU513xprQo6zOtma+z2xicU6pqZCsPb+5o5/Z84lybINi+t4iNLSUgQHB6OkpARBQUEOe55Fi8SEfImJosak/vBUd5KVJfri7Nwpfu7VSww3HzrU+P5r9uVqTpQKiP4M9a2aMVgzNNxRdMshAYAkgpb65O3Mq0hbRydZaoIaumirQafY9Q8PQZ+YUM3j64cVa15vXkmV3UKmsXJ6SRJ2zR2Jnccv6pVvbGI01v9+zuBvRb2/q0+MDX0/iWzBv7PGseX8zYDjoIBTUyOapM6dA1asAKZOdcjT2JUsixqnJ58UK1cDopbn1VeBsDDD/dUnSn9fBca9n270JOeMk5buCRuAS/o22Npx0Fww0O2kW3+BT2NByNV9OSwFtfpLd6iHyqtcFETNcYf3kzwf/84ajgHHAmcEnDVrgLvuEjMI5+YCbtg9yKRLl4C5c8Wq5YDop/PGG8A//mG6Fqo5X5VYE1bqSz9ViLs/Npzwsn6t12s/HcPSbfpTJFgTHtW1SQG+XiY7ftvLmn25mPvVIcgQo6UWTbA8KoQf8ETUELacv917dcomTD00/KGHmla4AYBWrUQH5KlTxQzIhw+L75cvF+tedetm+JimODLEXhoyOsmatvi8kiq8v81w/idLxza2arejhqOqOxmrn0qGdaNCdPtqcHp/InIEB86p23xlZAB79gC+viIgNFXDhgH794smKqUS2L4d6N1bNGEZmySwKY4MUWvMxFu2Tv8PWDf6KqewwujwamPz9Oi+DmOrdjtqSYDGjgrh9P5E5CgMOA7wzjvi66RJYuXvpszHR8x6fOQIcNNNom/RG2+I2ZDT0oBKDxjd2JiT7Jp9uRj3frrN0/8DhsNqh3eJ0AtZxoZ1A6bn6QGsm0zQnhoz9Lwpr8tERO6PAcfO/voLWLtWfP/YY64tiz3FxQHffiuGkffpA5SUiMkC4+OBZ54BnLz6hd005iRr7fT/5qhrvXYev2gQstS1POoAIcHyPD2mQhHgmOGojZnfiHOCEJEjsQ+Onb3/vljz6dprxfBwTyJJwOjRwA03AKtWAQsWADk5wMsvi9qcW28VHatTUkQ/nsaqrRVBqqRErMReXm55q6oSy2GEhIjO0V26AIMGAVKA8X4etvSfqd9XxJrp/61hbnZT9c+A6N8S4m9+TY36c92oOXJm2Yb2vzK1Fpi/L6+7iKjxGHDsqLJSLGYJeFbtTX0KBTB5MnDnnWLF8vffB7ZtAzZsEJskAf36Ad27A506iWY6Pz/RJ+nKFaCiQoSRsjIRXNQBpv7Xigr7lDewdy7CRh+CJP1dC5KSgPtTRA2LtRNvGRsKXVxZY/BcDaklMRWyMk9fxtyvDundPverQxY78NafTLDyqsqm4NGQTr8NmeDN1FpgtgZE8iwqlfi/X1YmPicqKsTnRnU1cPWq+Grue3P31dUB3t5iolMvL+33xm5zxPe6X4OCxEVYq1bis5HsjwHHjr78EigqEs05t97q6tI4no+PWN7h9ttFH53ly4GNG8WkgZmZYrOHgAAgOFh8IAQGip8DA41vfn7iQ7GkBMjLA/44UYWyEYc0nYBlAC9vysJnL0dg6p1KTJxoeXZfYzUs8746ZHQFyafHdLX5RG8qZF2uumrQyVgGkHn6Mm7uY/45GjqjrDMXAuSsrp5HpRL/9y5fFltRkfGvpaXaAKO+2FF/dVS/Pq+WVfAOrUDt5QDUlbnXQIiWLUXQiYoCOnQQfRzVXzt2BCIj3XuiWHfFgGNHkZFA377AlCkipTcnPXqIFcpfe01MbrhnD3DyJHDihPhQU19JtWihH1BCQkR40f2q/r7aqwqXqivQKbLhw4fTT1Xg7o/1b5MUMg6cqkTGQ0o89hhwyy2xWDgxAh36VKJTpGFNh6mmKGNDnHq3DbG5jKaWUAjwM/7f01EfdM5eCJALNDYd5eXigsHcdvGiGF1ZZ7xizmYKhTjxBwSIzw1fX3EBo950f7Z030lVLraWaedqSglOQHe/WNTWivLW1UHzvbHbLN1v62PUze9FRSIUlpWJ7fRpYPduw/dCqTQMPp06ia4QSv53MYkBx45uuQW4+Wb7/Qdvqtq2FWtcNYa9ahKM1RIoJAnzH/XH+i+BAweAdeuAdeuUaNVKiUmTREAdMEAbJIweAwDsWPtgrB9LXkkVJOjnKEkC+tVbt8perO2PZM95a5wxfxLn2THvyhVxYs3J0W5//aUfXsrKbDumvz8QGiq2sDD9r6Gh4gKmZUtxkaP+qvt9y5Yi1NgjzIuJOPXnatpamoUX5rp+BW+VSjTHX7oktr/+Av78Ezh1Srvl5oq+hYcPi01XTAzw0kvAPfeIQEj6OJOxA9eiooZpyMzA5pibZfngQeD//g/4z3+A/HztY7p1EzM333MPEBtr/BgA9G57enRXJLQLtuuJ1JlNRnklVRiSttUgUKXPvU5v+Qhnlccemlp5HaG2VoxyzMkxDDI5OSLAWCMgQDShqLfoaO33kZFi1vaICBFkWrRw6EuyibWzhrurmhrgzBkRdnTDT0aG9nfXt6+oPU9JcWlRnYJLNVjAgOPeHPGBZGlpgNpaYMsWsRbX+vXiigkQJ/jrrgOmTQM6DbqMrILLGBgXqlnwUn3cg+eK8eqPx8yeSC3VJJi631nLGhgNOADS512nqVGyZ/B0tKZW3saoqxPh5dgx7XbqlAgwZ89arlUODBRTPqi32FjDINOypVNeit156t/BlSvAkiXAK6+I5i4ASE0FFi8WE7J6Ki7VQE2aIzqfWup06+0tPhxSU0UHyHXrgM8/F7M3b9kCZFzMRVjqIUgKcdJPG5+AuwbFao45+ZM9ZvuuWKpJMHd/QzsM28rYzMkyoGmiasiSFK7U1MprjfJyIDtbP8gcOwYcPy76uJni6ysGP+iGGN0tLMxzO7F6al+vFi3EJKz33iuaqZYuBX76Cdi0SVyQvfAC0K6dq0vpWgw45HZc/YEUFCQ+IKZNE1fFH6yowpoq7agpGcDc/2Xh2LYIPDpDiZzL5k+kljrvOrtzrymWgmVTG/XU1MqrJsuio746vOgGmr/+Mv04Pz+ga1fRvNqtm+iEqg4wUVHNu4+GJ6+V16oV8NZbwKxZwL//LRZ6Xr4cWL0a+Ne/gDlzxGdac8SAQ27JXT6Q4uKA2/5RgTX1RmJBIWPR0kq8+pwSE/4RAClMvzOw7onUUk2Cu9Q07Dx+UW/JCUmCXrB0dfC0lbuX98oVMdKwfm1MdraoqTGldWttiNHdYmOb3+hNWzirJtRVOnbUhpqnngJ++UU0X330EfDss8ADD4ipPZoTBhxyW+7ygWSsJkCChC5R/jh0FvjyYyUCeyeg1egsQJKhqHcibQo1I/VXBQcASYZmNmW1hgZPV41kcnVQlmXgwgXRhFS/aSknR4yiMcbLS5yw6oeYrl1FcxKRKUlJwI4dwDffiNqb7GzgkUdEf520NGD8eM9tjqyPAYfIAlM1AXemKZGeDrz3HvC//8Xir5wIeIdUItjLHwegxMBWYs4KSzUJ7lDTYGquH2O1SLYGT1ePZHJGUL58WYSYEyfEpvt9aanpxwUFiRm/dQNMt24i3HB2Ww7xbyhJAm67DbjxRuDTT0UNzokTYlLWIUPEiKshQ1xdSsfjKKrm2jhJNjM3mikvT1QFf/ihduimJAGjRolV5ceNA64ozI+GcsRoKWtPEI4aaeJJI1hKSsQw3foB5vhxMYeJKZIk5ivRDTLqrU2b5nM1bStXB2NPUlYGvP662NQzRY8fDyxaBHTu7Nqy2YrDxC1gwCFHqakBvvsOWLZMjGZQ8/ERI7QmThQLloaH235sW69mbT1BmJsvqKGayhwkdXWiKen8edGR9/Rpw6242PwxoqLE4q6dO4tN/X3Hjtp5YVgjYR1PCsbu5Px5UZvz2WeiedTbG3jwQWDhQjGHUVPAgGMBAw45w59/ilXX16wBDumsmSlJYqX5lBRg5Egxa7KlwGNrWGnoCcLetUiuPlFdvSpqVy5eFDVr58+LEUrnz+tv+fnWzUAeHq4fXtTfd+ok5pIxhzUS1msqwbipOnxY9M/5/nvxc8uWwNy5wOOPi1mo3RkDjgUMOORsR46IoLN+vX7YUYuLE0GnWzdx0uzUSXwNDwfyS20PCaZOEM/c1B039Y5y6lWwvWqG1GHl0iWgsFC76f5c/z5blhhQKESTUdu2Ymh1+/bi96Le2re3HGJMcXXQa2r4fjnHtm3Ak08Cv/8ufm7bVsyp849/uO+IPAYcCxhwyJXy88XkgZs3i4X1jh83vW9wMBDdtxCVgw3DygOdB6N/TCuEhooFStVfvb2NnyDU6tce2KPZxJpZmtU1Q60DlXorTutuxkKK+mdznXXNUSi0KzVHR4utbVvt9+qtdWvx3jkCayRs54gmUzKkUonh5fPniyUhADET8uLFolnd3TDgWMCAQ/bU2IBQXCyuoH7/XXRaVa/CfvasuN+rZRXaPrgVks5EbbJKwrllI1FXZvh8gYEi7Ch75aI6QQxd1yyj/DcJEu5rMxL5uIjvCrSrLE/rlYBRcbHw9hYney8v7VeVSrsa8tWrorNiZSWwPTcX685qj5HsnYDoK7EoLTUeYkpKgIZ+6igUYph0eLjYWrXSfm/q55AQ109yxxqJhnHWMiUk5mVaulTU4Kj7m11/vQg6ffu6smT6GHAsYMAhe3Fkv4qqKtGP59w54Lujufg2LwsyZECWEJPfC15nYnH5svgwunzZeHOMV8sq+HfJQ1jKUYP7LqxPRMRt+60OTsbYGr506a44rd50w4mxwOIOYaU+awMuaySoKSgqEhMEvvuuuJCRJNFk9eKLYjJJV2PAsYABh+zB2Vfl1iwYqm76KS4W35eVAecuV+H1bP1FNCFL6HSxL0623m9wHO+dg6HKb4XaWm2NTW2tqMVR1+j4+IjVpb2jC3E50bDp5dq6wejYspUmuISFGYYZT5jnpSGdv1kjQU1BTo5Y+mHVKvGzn5/ohDxvnmg6dxUGHAsYcMgemlK/CmO1B8O7RDQ6oDXnppfm/Nqp+fjtN7H0w/bt4udWrYAFC4CZM11zkWLL+dvNKnuJmg71Egu63HUxx4kDY7Fr7kismjEYu+aOxMSBsZoZlL3+nmmuITMo2+MYTZW5NcSIPMWAAcDWrcC334rJKi9dEjU5PXoAa9c2vD+dM7AGhzU41Aie0K/CHs0mzbHphTU41NzU1oqVyhcuFKNBAbH21euvA8OGOacMbKKygAGH7Kk5ntxJ8ISAa0+cqbl5KC8H3nhDrGlVUSFuGztWLP3Qtatjn5sBxwIGHCKyFwZcgTM1Nz/5+cBzzwGffCIGJHh5AfffL5aDaNPGMc/JPjhERE4SFaxEcsdWzTrc5JVUacINAKhkYP66LOSVVLm2YORQkZFi3b1Dh4BbbxUh54MPxEzsL72krd1xFQYcIiJqFHa4bt66dwe+/lqMtBo4UDRhLVgA3Hyza8vFgENERI3SlEYUkuNcey2wZ49Y+iE+Hnj0UdeWhwGHiIgapTlPF0D6FApg4kTg2DHR8diVHLS0HBERNScTB8ZieJcIdrgmAO4xUzkDDhER2UVUsJLBhtwGm6iIiIjI4zg04BQVFWHy5MkICgpCSEgI7r33XpSXl5vc//Tp05Akyei2du1azX7G7l+9erUjXwoRERE1IQ5topo8eTLy8vKwefNm1NTUYPr06bj//vuxcuVKo/vHxMQgLy9P77aPPvoIr732GsaMGaN3+/LlyzF69GjNzyEhIXYvPxGRvXCWXyLncljAOXr0KDZu3Ih9+/ZhwIABAIB3330XN954I15//XVER0cbPMbLywuRkZF6t61fvx533nknAgMD9W4PCQkx2JeIyB1xll8i53NYE9Xu3bsREhKiCTcAkJKSAoVCgYyMDKuOkZmZiQMHDuDee+81uO/hhx9GeHg4Bg0ahM8++wzNcMUJImoCOMsvkWs4rAYnPz8frVu31n8yb2+EhYUhX70MqQWffvopunfvjiFDhujd/sILL+C6666Dv78/Nm3ahIceegjl5eV41MSsQtXV1aiurtb8XFpaauOrISJqGHOz/LKpishxbK7BmTt3rsmOwOrt2LFjjS5YVVUVVq5cabT2ZsGCBRg6dCgSExMxZ84cPP3003jttddMHistLQ3BwcGaLSYmptHlIyKyBmf5JXINmwPOE088gaNHj5rdOnTogMjISFy4cEHvsbW1tSgqKrKq78z//vc/VFZWYsqUKRb3TUpKwl9//aVXS6Nr3rx5KCkp0Wxnz5617sUSETUSZ/klcg2bm6giIiIQERFhcb/k5GQUFxcjMzMT/fv3BwBs3boVKpUKSUlJFh//6aef4tZbb7XquQ4cOIDQ0FD4+fkZvd/Pz8/kfUREjsZZfomcz2F9cLp3747Ro0djxowZWLZsGWpqajBr1izcddddmhFU586dw6hRo/DFF19g0KBBmseePHkSO3fuxA8//GBw3G+//RYFBQUYPHgwWrRogc2bN+OVV17Bk08+6aiXQkTUaJzll8i5HDoPzpdffolZs2Zh1KhRUCgUmDBhApYsWaK5v6amBtnZ2aisrNR73GeffYZ27drhhhtuMDimj48Pli5din/961+QZRmdOnXCm2++iRkzZjjypRAREVETIsnNcHx1aWkpgoODUVJSgqCgIFcXh4iIiKxgy/mba1EREZmQV1KF9FOFnLOGqAniauJEREZw9mGipo01OERE9XD2YaKmjwGHiKgec7MPE1HTwIBDRFQPZx92HPZrImdhwCEiqoezDzvGmn25GLpoK+7+OANDF23Fmn25ri4SeTAOE+cwcSKPlFdShZzCCsSHBzQ4mOSVVHH2YTvJK6nC0EVb9Zr+vCQJu+aO5HtLVrPl/M1RVETkcew1AoqzD9sPV1UnZ2MTFRF5FI6Ack/s10TOxoBDRB6FI6DcE/s1kbOxiYqIPIq6pqB+Xw/WFLgeV1UnZ2INDhF5FNYUuLeoYCWSO7bi74McjjU4RORxWFNARAw4ROSROAKKqHljExURERF5HAYcIiIi8jgMOERERORxGHCIiIjI4zDgEBERkcdhwCEiIiKPw4BDREREHocBh4iIiDwOAw4RERF5HAYcIiIi8jgMOERERORxGHCIiIjI4zDgEBERkcdhwCEiIiKPw4BDREREHocBh4iIiDwOAw4RERF5HAYcIiIi8jgMOERERORxGHCIiIjI4zDgEBERkcdhwCEiIiKPw4BDREREHocBh4iIiDwOAw4RERF5HIcFnJdffhlDhgyBv78/QkJCrHqMLMtYuHAhoqKioFQqkZKSghMnTujtU1RUhMmTJyMoKAghISG49957UV5e7oBXQERERE2VwwLO1atXcccdd2DmzJlWP2bx4sVYsmQJli1bhoyMDAQEBCA1NRVXrlzR7DN58mQcPnwYmzdvxnfffYedO3fi/vvvd8RLICIioiZKkmVZduQTrFixAo8//jiKi4vN7ifLMqKjo/HEE0/gySefBACUlJSgTZs2WLFiBe666y4cPXoUPXr0wL59+zBgwAAAwMaNG3HjjTfir7/+QnR0tFVlKi0tRXBwMEpKShAUFNSo10dERETOYcv522364OTk5CA/Px8pKSma24KDg5GUlITdu3cDAHbv3o2QkBBNuAGAlJQUKBQKZGRkmDx2dXU1SktL9TYiIiLyXG4TcPLz8wEAbdq00bu9TZs2mvvy8/PRunVrvfu9vb0RFham2ceYtLQ0BAcHa7aYmBg7l56IqHnKK6lC+qlC5JVUubooRHpsCjhz586FJElmt2PHjjmqrA02b948lJSUaLazZ8+6ukhERE3emn25GLpoK+7+OANDF23Fmn25ri4SkYa3LTs/8cQTmDZtmtl9OnTo0KCCREZGAgAKCgoQFRWlub2goAB9+/bV7HPhwgW9x9XW1qKoqEjzeGP8/Pzg5+fXoHIREZGhvJIqzFt3CKq/e3GqZGD+uiwM7xKBqGClawtHBBsDTkREBCIiIhxSkPj4eERGRmLLli2aQFNaWoqMjAzNSKzk5GQUFxcjMzMT/fv3BwBs3boVKpUKSUlJDikXEZGz5JVUIaewAvHhAW4fEnIKKzThRq1OlnG6sNLty07Ng00Bxxa5ubkoKipCbm4u6urqcODAAQBAp06dEBgYCADo1q0b0tLSMG7cOEiShMcffxwvvfQSOnfujPj4eCxYsADR0dEYO3YsAKB79+4YPXo0ZsyYgWXLlqGmpgazZs3CXXfdZfUIKiIid7RmX66mRkQhAWnjEzBxYKyri2VSfHgAFBL0Qo6XJCEu3N91hSLS4bCAs3DhQnz++eeanxMTEwEA27Ztw4gRIwAA2dnZKCkp0ezz9NNPo6KiAvfffz+Ki4sxbNgwbNy4ES1atNDs8+WXX2LWrFkYNWoUFAoFJkyYgCVLljjqZRAROVxTbO6JClYibXwC5q/LQp0sw0uS8Mr4Xm5bXmp+HD4PjjviPDhE5E7STxXi7o8Np7pYNWMwkju2ckGJrJdXUoXThZWIC/dnuCGHs+X87bAaHCIisk5Tbu6JClYy2JBbcpt5cIiImit1c4+XJAEAm3uI7IA1OEREbmDiwFgM7xLB5h4iO2HAISJyE2zuIbIfNlERERGRx2HAISIiIo/DgENEREQehwGHiIiIPA4DDhEREXkcBhwiIiLyOAw4RERE5HEYcIiIiMjjMOAQERGRx2HAISIiIo/DgENEREQehwGHiIiIPA4DDhEREXkcBhwiIiLyOAw4RERE5HEYcIiIiMjjMOAQERGRx2HAISIiIo/DgENEREQehwGHiNxeXkkV0k8VIq+kytVFIaImwtvVBSAiMmfNvlzMW3cIKhlQSEDa+ARMHBjr6mIRkZtjDQ4Rua28kipNuAEAlQzMX5fFmhwisogBh4jcVk5hhSbcqNXJMk4XVrqmQETUZDDgEJHbig8PgELSv81LkhAX7u+aAhFRk8GAQ0RuKypYibTxCfCSRMrxkiS8Mr4XooKVLi4ZEbk7djImIrc2cWAshneJwOnCSsSF+zPcEJFVGHCIyO1FBSsZbIjIJmyiIiIiIo/DgENEREQehwGHiIiIPA4DDhEREXkcBhwiIiLyOAw4RERE5HEYcIiIiMjjOCzgvPzyyxgyZAj8/f0REhJicf+amhrMmTMHCQkJCAgIQHR0NKZMmYLz58/r7RcXFwdJkvS2RYsWOehVEBERUVPksIBz9epV3HHHHZg5c6ZV+1dWVuL333/HggUL8Pvvv2PdunXIzs7GrbfearDvCy+8gLy8PM32yCOP2Lv4RERE1IQ5bCbj559/HgCwYsUKq/YPDg7G5s2b9W577733MGjQIOTm5iI2NlZze8uWLREZGWm3shIREZFnces+OCUlJZAkyaCJa9GiRWjVqhUSExPx2muvoba21uxxqqurUVpaqrcRERGR53LbtaiuXLmCOXPmYNKkSQgKCtLc/uijj6Jfv34ICwtDeno65s2bh7y8PLz55psmj5WWlqapUdLFoENERNR0qM/bsixb3lm2wZw5c2QAZrejR4/qPWb58uVycHCwLU8jX716Vb7lllvkxMREuaSkxOy+n376qezt7S1fuXLF5D5XrlyRS0pKNNuRI0csvg5u3Lhx48aNm3tuZ8+etZglbKrBeeKJJzBt2jSz+3To0MGWQxqoqanBnXfeiTNnzmDr1q16tTfGJCUloba2FqdPn0bXrl2N7uPn5wc/Pz/Nz4GBgTh79ixatmwJSZIaVV5PUFpaipiYGJw9e9bi+00Nw/fYsfj+Oh7fY8fje2yZLMsoKytDdHS0xX1tCjgRERGIiIhocMEsUYebEydOYNu2bWjVqpXFxxw4cAAKhQKtW7e2+nkUCgXatWvXmKJ6pKCgIP6ncjC+x47F99fx+B47Ht9j84KDg63az2F9cHJzc1FUVITc3FzU1dXhwIEDAIBOnTohMDAQANCtWzekpaVh3LhxqKmpwe23347ff/8d3333Herq6pCfnw8ACAsLg6+vL3bv3o2MjAyMHDkSLVu2xO7du/Gvf/0L99xzD0JDQx31UoiIiKiJcVjAWbhwIT7//HPNz4mJiQCAbdu2YcSIEQCA7OxslJSUAADOnTuHb775BgDQt29fvWOpH+Pn54fVq1fjueeeQ3V1NeLj4/Gvf/0Ls2fPdtTLICIioibIYQFnxYoVFufAkXV6QcfFxVnsFd2vXz/s2bPHHsUjHX5+fnj22Wf1+imRffE9diy+v47H99jx+B7blyRbShVERERETYxbT/RHRERE1BAMOERERORxGHCIiIjI4zDgeIidO3filltuQXR0NCRJwoYNG/Tul2UZCxcuRFRUFJRKJVJSUnDixAm9fYqKijB58mQEBQUhJCQE9957L8rLy/X2OXjwIK655hq0aNECMTExWLx4saNfmltIS0vDwIED0bJlS7Ru3Rpjx45Fdna23j5XrlzBww8/jFatWiEwMBATJkxAQUGB3j65ubm46aab4O/vj9atW+Opp54yWEtt+/bt6NevH/z8/NCpUyerF6xt6j744AP07t1bMwdIcnIyfvzxR839fH/ta9GiRZAkCY8//rjmNr7Hjffcc89BkiS9rVu3bpr7+R47kQ0rKJAb++GHH+R///vf8rp162QA8vr16/XuX7RokRwcHCxv2LBB/uOPP+Rbb71Vjo+Pl6uqqjT7jB49Wu7Tp4+8Z88e+ZdffpE7deokT5o0SXN/SUmJ3KZNG3ny5MlyVlaWvGrVKlmpVMoffvihs16my6SmpsrLly+Xs7Ky5AMHDsg33nijHBsbK5eXl2v2efDBB+WYmBh5y5Yt8m+//SYPHjxYHjJkiOb+2tpauVevXnJKSoq8f/9++YcffpDDw8PlefPmafb5888/ZX9/f3n27NnykSNH5HfffVf28vKSN27c6NTX6wrffPON/P3338vHjx+Xs7Oz5fnz58s+Pj5yVlaWLMt8f+1p7969clxcnNy7d2/5scce09zO97jxnn32Wblnz55yXl6eZrt48aLmfr7HzsOA44HqBxyVSiVHRkbKr732mua24uJi2c/PT161apUsy7Jmfa59+/Zp9vnxxx9lSZLkc+fOybIsy++//74cGhoqV1dXa/aZM2eO3LVrVwe/Ivdz4cIFGYC8Y8cOWZbF++nj4yOvXbtWs8/Ro0dlAPLu3btlWRYhVKFQyPn5+Zp9PvjgAzkoKEjznj799NNyz5499Z5r4sSJcmpqqqNfklsKDQ2VP/nkE76/dlRWViZ37txZ3rx5s3zttddqAg7fY/t49tln5T59+hi9j++xc7GJqhnIyclBfn4+UlJSNLcFBwcjKSkJu3fvBgDs3r0bISEhGDBggGaflJQUKBQKZGRkaPYZPnw4fH19NfukpqYiOzsbly9fdtKrcQ/qCSrDwsIAAJmZmaipqdF7j7t164bY2Fi99zghIQFt2rTR7JOamorS0lIcPnxYs4/uMdT7qI/RXNTV1WH16tWoqKhAcnIy3187evjhh3HTTTcZvA98j+3nxIkTiI6ORocOHTB58mTk5uYC4HvsbA6b6I/ch3rJC93/MOqf1ffl5+cbrOfl7e2NsLAwvX3i4+MNjqG+r7ksl6FSqfD4449j6NCh6NWrFwDx+n19fRESEqK3b/332NjvQH2fuX1KS0tRVVUFpVLpiJfkNg4dOoTk5GRcuXIFgYGBWL9+PXr06IEDBw7w/bWD1atX4/fff8e+ffsM7uPfsH0kJSVhxYoV6Nq1K/Ly8vD888/jmmuuQVZWFt9jJ2PAIbLRww8/jKysLOzatcvVRfE4Xbt2xYEDB1BSUoL//e9/mDp1Knbs2OHqYnmEs2fP4rHHHsPmzZvRokULVxfHY40ZM0bzfe/evZGUlIT27dvjv//9L4OHk7GJqhmIjIwEAIOe+gUFBZr7IiMjceHCBb37a2trUVRUpLePsWPoPoenmzVrFr777jts27ZNb0X6yMhIXL16FcXFxXr713+PLb1/pvYJCgpqFh+Ovr6+6NSpE/r374+0tDT06dMH77zzDt9fO8jMzMSFCxfQr18/eHt7w9vbGzt27MCSJUvg7e2NNm3a8D12gJCQEHTp0gUnT57k37GTMeA0A/Hx8YiMjMSWLVs0t5WWliIjIwPJyckAgOTkZBQXFyMzM1Ozz9atW6FSqZCUlKTZZ+fOnaipqdHss3nzZnTt2tXjm6dkWcasWbOwfv16bN261aCprn///vDx8dF7j7Ozs5Gbm6v3Hh86dEgvSG7evBlBQUHo0aOHZh/dY6j3UR+juVGpVKiurub7awejRo3CoUOHcODAAc02YMAATJ48WfM932P7Ky8vx6lTpxAVFcW/Y2dzdS9nso+ysjJ5//798v79+2UA8ptvvinv379fPnPmjCzLYph4SEiI/PXXX8sHDx6Ub7vtNqPDxBMTE+WMjAx5165dcufOnfWGiRcXF8tt2rSR//GPf8hZWVny6tWrZX9//2YxTHzmzJlycHCwvH37dr3hn5WVlZp9HnzwQTk2NlbeunWr/Ntvv8nJyclycnKy5n718M8bbrhBPnDggLxx40Y5IiLC6PDPp556Sj569Ki8dOnSZjP8c+7cufKOHTvknJwc+eDBg/LcuXNlSZLkTZs2ybLM99cRdEdRyTLfY3t44okn5O3bt8s5OTnyr7/+KqekpMjh4eHyhQsXZFnme+xMDDgeYtu2bTIAg23q1KmyLIuh4gsWLJDbtGkj+/n5yaNGjZKzs7P1jnHp0iV50qRJcmBgoBwUFCRPnz5dLisr09vnjz/+kIcNGyb7+fnJbdu2lRctWuSsl+hSxt5bAPLy5cs1+1RVVckPPfSQHBoaKvv7+8vjxo2T8/Ly9I5z+vRpecyYMbJSqZTDw8PlJ554Qq6pqdHbZ9u2bXLfvn1lX19fuUOHDnrP4cn++c9/yu3bt5d9fX3liIgIedSoUZpwI8t8fx2hfsDhe9x4EydOlKOiomRfX1+5bdu28sSJE+WTJ09q7ud77DxcTZyIiIg8DvvgEBERkcdhwCEiIiKPw4BDREREHocBh4iIiDwOAw4RERF5HAYcIiIi8jgMOERERORxGHCIiIjI4zDgEBH9bcSIEXj88cet3n/atGkYO3asw8pDRA3HgENEFl28eBEzZ85EbGws/Pz8EBkZidTUVPz6669WH0OSJINt2LBhAIDTp09DkiQcOHBAs39cXJzRx6i3adOmWXy+DRs26N22YsUKhISEmHzMunXr8OKLL1r9mt555x2sWLHC6v2JyHm8XV0AInJ/EyZMwNWrV/H555+jQ4cOKCgowJYtW3Dp0iWbjrN8+XKMHj1a87Ovr6/Jffft24e6ujoAQHp6OiZMmIDs7GwEBQUBAJRKZQNeiXlhYWE27R8cHGz3MhCRfbAGh4jMKi4uxi+//IJXX30VI0eORPv27TFo0CDMmzcPt956KwDgxIkTGD58OFq0aIEePXpg8+bNRmtQQkJCEBkZqdnUgSI+Ph4AkJiYCEmSMGLECERERBjs17p1a81tK1euRMeOHeHr64uuXbvi//7v/zTPExcXBwAYN24cJEnS/GyJbhPV/PnzkZSUZLBPnz598MILLwAwbKIaMWIEHn30UTz99NMICwtDZGQknnvuOb3HHzt2DMOGDdO8Vz///LPR94qIGocBh4jMCgwMRGBgIDZs2IDq6mqD+1UqFcaPHw9fX19kZGRg2bJlmDNnjk3PsXfvXgDAzz//jLy8PKxbt87s/uvXr8djjz2GJ554AllZWXjggQcwffp0bNu2DYCo/QFEjVFeXp7mZ1tMnjwZe/fuxalTpzS3HT58GAcPHsTdd99t8nGff/45AgICkJGRgcWLF+OFF17A5s2bAQB1dXUYO3Ys/P39kZGRgY8++gj//ve/bS4bEVnGgENEZnl7e2PFihX4/PPPERISgqFDh2L+/Pk4ePAgABFKjh07hi+++AJ9+vTB8OHD8corrxg91qRJkzSBSR2aACAiIgIA0KpVK70aG1Nef/11TJs2DQ899BC6dOmC2bNnY/z48Xj99df1jqeuMVL/bIuePXuiT58+WLlypea2L7/8EklJSejUqZPJx/Xu3RvPPvssOnfujClTpmDAgAHYsmULAGDz5s04deqU5r0aNmwYXn75ZZvLRkSWMeAQkUUTJkzA+fPn8c0332D06NHYvn07+vXrhxUrVuDo0aOIiYlBdHS0Zv/k5GSjx3nrrbdw4MABzXb99dc3qDxHjx7F0KFD9W4bOnQojh492qDjmTJ58mRNwJFlGatWrcLkyZPNPqZ37956P0dFReHChQsAgOzsbMTExCAyMlJz/6BBg+xaZiISGHCIyCotWrTA9ddfjwULFiA9PR3Tpk3Ds88+a9MxIiMj0alTJ80WEBDgoNLax6RJk5CdnY3ff/8d6enpOHv2LCZOnGj2MT4+Pno/S5IElUrlyGISkREMOETUID169EBFRQW6d++Os2fPIi8vT3Pfnj17bDqWejSVetSUJd27dzcYov7rr7+iR48emp99fHysPp4p7dq1w7XXXosvv/wSX375Ja6//nq0bt26wcfr2rUrzp49i4KCAs1tDekfRESWcZg4EZl16dIl3HHHHfjnP/+J3r17o2XLlvjtt9+wePFi3HbbbUhJSUGXLl0wdepUvPbaaygtLbW542zr1q2hVCqxceNGtGvXDi1atDA7BPupp57CnXfeicTERKSkpODbb7/FunXr8PPPP2v2iYuLw5YtWzB06FD4+fkhNDQUgAhRuvPtAICfnx+6d+9u9LkmT56MZ599FlevXsVbb71l0+uq7/rrr0fHjh0xdepULF68GGVlZXjmmWcAiJoeIrIf1uAQkVmBgYFISkrCW2+9heHDh6NXr15YsGABZsyYgffeew8KhQLr169HVVUVBg0ahPvuu8/mjrPe3t5YsmQJPvzwQ0RHR+O2224zu//YsWPxzjvv4PXXX0fPnj3x4YcfYvny5RgxYoRmnzfeeAObN29GTEwMEhMTNbeXl5cjMTFRb7vllltMPtftt9+OS5cuobKystGzFnt5eWHDhg0oLy/HwIEDcd9992nCYIsWLRp1bCLSJ8myLLu6EETkeSRJwvr167mUgQW//vorhg0bhpMnT6Jjx46uLg6Rx2ATFRGRE61fvx6BgYHo3LkzTp48icceewxDhw5luCGyMwYcIiInKisrw5w5c5Cbm4vw8HCkpKTgjTfecHWxiDwOm6iIiIjI47CTMREREXkcBhwiIiLyOAw4RERE5HEYcIiIiMjjMOAQERGRx2HAISIiIo/DgENEREQehwGHiIiIPA4DDhEREXmc/wf/VnVCSqMH4AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_sm.plot_partial(0, cpr=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using pyGAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:11.139860Z",
     "iopub.status.busy": "2022-04-26T19:56:11.139565Z",
     "iopub.status.idle": "2022-04-26T19:56:11.307197Z",
     "shell.execute_reply": "2022-04-26T19:56:11.305892Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (11 of 11) |########################| Elapsed Time: 0:00:00 Time:  0:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearGAM                                                                                                 \n",
      "=============================================== ==========================================================\n",
      "Distribution:                        NormalDist Effective DoF:                                      7.6772\n",
      "Link Function:                     IdentityLink Log Likelihood:                                 -7833.1159\n",
      "Number of Samples:                          313 AIC:                                            15683.5863\n",
      "                                                AICc:                                             15684.14\n",
      "                                                GCV:                                      30838885095.1676\n",
      "                                                Scale:                                    29480381715.8292\n",
      "                                                Pseudo R-Squared:                                   0.8117\n",
      "==========================================================================================================\n",
      "Feature Function                  Lambda               Rank         EDoF         P > x        Sig. Code   \n",
      "================================= ==================== ============ ============ ============ ============\n",
      "s(0)                              [15.8489]            12           4.3          1.11e-16     ***         \n",
      "l(1)                              [15.8489]            1            0.9          2.35e-10     ***         \n",
      "l(2)                              [15.8489]            1            0.8          8.45e-01                 \n",
      "l(3)                              [15.8489]            1            0.9          3.79e-01                 \n",
      "l(4)                              [15.8489]            1            0.8          1.11e-16     ***         \n",
      "intercept                                              1            0.0          9.14e-01                 \n",
      "==========================================================================================================\n",
      "Significance codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
      "\n",
      "WARNING: Fitting splines and a linear function to a feature introduces a model identifiability problem\n",
      "         which can cause p-values to appear significant when they are not.\n",
      "\n",
      "WARNING: p-values calculated in this manner behave correctly for un-penalized models or models with\n",
      "         known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n",
      "         are typically lower than they should be, meaning that the tests reject the null too readily.\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_63/661170992.py:10: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n",
      " \n",
      "Please do not make inferences based on these values! \n",
      "\n",
      "Collaborate on a solution, and stay up to date at: \n",
      "github.com/dswah/pyGAM/issues/163 \n",
      "\n",
      "  print(gam.summary())\n"
     ]
    }
   ],
   "source": [
    "predictors = ['SqFtTotLiving', 'SqFtLot', 'Bathrooms', \n",
    "              'Bedrooms', 'BldgGrade']\n",
    "outcome = 'AdjSalePrice'\n",
    "X = house_98105[predictors].values\n",
    "y = house_98105[outcome]\n",
    "\n",
    "## model\n",
    "gam = LinearGAM(s(0, n_splines=12) + l(1) + l(2) + l(3) + l(4))\n",
    "gam.gridsearch(X, y)\n",
    "print(gam.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:11.310781Z",
     "iopub.status.busy": "2022-04-26T19:56:11.310557Z",
     "iopub.status.idle": "2022-04-26T19:56:11.815299Z",
     "shell.execute_reply": "2022-04-26T19:56:11.814418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(figsize=(8, 8), ncols=2, nrows=3)\n",
    "\n",
    "titles = ['SqFtTotLiving', 'SqFtLot', 'Bathrooms', 'Bedrooms', 'BldgGrade']\n",
    "for i, title in enumerate(titles):\n",
    "    ax = axes[i // 2, i % 2]\n",
    "    XX = gam.generate_X_grid(term=i)\n",
    "    ax.plot(XX[:, i], gam.partial_dependence(term=i, X=XX))\n",
    "    ax.plot(XX[:, i], gam.partial_dependence(term=i, X=XX, width=.95)[1], c='r', ls='--')\n",
    "    ax.set_title(titles[i]);\n",
    "    \n",
    "axes[2][1].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Additional material - not in book\n",
    "# Regularization\n",
    "## Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:11.818542Z",
     "iopub.status.busy": "2022-04-26T19:56:11.818285Z",
     "iopub.status.idle": "2022-04-26T19:56:11.822331Z",
     "shell.execute_reply": "2022-04-26T19:56:11.821601Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso, LassoLars, LassoCV, LassoLarsCV\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:11.825293Z",
     "iopub.status.busy": "2022-04-26T19:56:11.825039Z",
     "iopub.status.idle": "2022-04-26T19:56:11.883986Z",
     "shell.execute_reply": "2022-04-26T19:56:11.882644Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   AdjSalePrice  SqFtTotLiving  SqFtLot  Bathrooms  Bedrooms  BldgGrade\n",
      "1      300805.0           2400     9373       3.00         6          7\n",
      "2     1076162.0           3764    20156       3.75         4         10\n",
      "3      761805.0           2060    26036       1.75         4          8\n",
      "4      442065.0           3200     8618       3.75         5          7\n",
      "5      297065.0           1720     8620       1.75         4          7\n"
     ]
    }
   ],
   "source": [
    "subset = ['AdjSalePrice', 'SqFtTotLiving', 'SqFtLot', 'Bathrooms', \n",
    "          'Bedrooms', 'BldgGrade']\n",
    "\n",
    "house = pd.read_csv(HOUSE_CSV, sep='\\t')\n",
    "print(house[subset].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:11.887063Z",
     "iopub.status.busy": "2022-04-26T19:56:11.886857Z",
     "iopub.status.idle": "2022-04-26T19:56:11.912149Z",
     "shell.execute_reply": "2022-04-26T19:56:11.911244Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression()\n"
     ]
    }
   ],
   "source": [
    "predictors = ['SqFtTotLiving', 'SqFtLot', 'Bathrooms', 'Bedrooms',\n",
    "              'BldgGrade', 'PropertyType', 'NbrLivingUnits',\n",
    "              'SqFtFinBasement', 'YrBuilt', 'YrRenovated', \n",
    "              'NewConstruction']\n",
    "outcome = 'AdjSalePrice'\n",
    "\n",
    "X = pd.get_dummies(house[predictors], drop_first=True)\n",
    "X['NewConstruction'] = [1 if nc else 0 for nc in X['NewConstruction']]\n",
    "columns = X.columns\n",
    "# X = StandardScaler().fit_transform(X * 1.0)\n",
    "y = house[outcome]\n",
    "\n",
    "house_lm = LinearRegression()\n",
    "print(house_lm.fit(X, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:11.915781Z",
     "iopub.status.busy": "2022-04-26T19:56:11.915556Z",
     "iopub.status.idle": "2022-04-26T19:56:11.970281Z",
     "shell.execute_reply": "2022-04-26T19:56:11.968996Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso(alpha=10)\n"
     ]
    }
   ],
   "source": [
    "house_lasso = Lasso(alpha=10)\n",
    "print(house_lasso.fit(X, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:11.975061Z",
     "iopub.status.busy": "2022-04-26T19:56:11.973890Z",
     "iopub.status.idle": "2022-04-26T19:56:12.580755Z",
     "shell.execute_reply": "2022-04-26T19:56:12.580040Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Method = LassoLars\n",
    "MethodCV = LassoLarsCV\n",
    "Method = Lasso\n",
    "MethodCV = LassoCV\n",
    "\n",
    "alpha_values = []\n",
    "results = []\n",
    "for alpha in [0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000]:\n",
    "    model = Method(alpha=alpha)\n",
    "    model.fit(X, y)\n",
    "    alpha_values.append(alpha)\n",
    "    results.append(model.coef_)\n",
    "modelCV = MethodCV(cv=5)\n",
    "modelCV.fit(X, y)\n",
    "ax = pd.DataFrame(results, index=alpha_values, columns=columns).plot(logx=True, legend=False)\n",
    "ax.axvline(modelCV.alpha_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:12.583354Z",
     "iopub.status.busy": "2022-04-26T19:56:12.583179Z",
     "iopub.status.idle": "2022-04-26T19:56:12.591183Z",
     "shell.execute_reply": "2022-04-26T19:56:12.590579Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SqFtTotLiving</td>\n",
       "      <td>289.048846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SqFtLot</td>\n",
       "      <td>0.029471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bathrooms</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bedrooms</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BldgGrade</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NbrLivingUnits</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SqFtFinBasement</td>\n",
       "      <td>3.316479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>YrBuilt</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>YrRenovated</td>\n",
       "      <td>45.727472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NewConstruction</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PropertyType_Single Family</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PropertyType_Townhouse</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          name        coef\n",
       "0                SqFtTotLiving  289.048846\n",
       "1                      SqFtLot    0.029471\n",
       "2                    Bathrooms    0.000000\n",
       "3                     Bedrooms   -0.000000\n",
       "4                    BldgGrade    0.000000\n",
       "5               NbrLivingUnits   -0.000000\n",
       "6              SqFtFinBasement    3.316479\n",
       "7                      YrBuilt   -0.000000\n",
       "8                  YrRenovated   45.727472\n",
       "9              NewConstruction   -0.000000\n",
       "10  PropertyType_Single Family   -0.000000\n",
       "11      PropertyType_Townhouse    0.000000"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\n",
    "    'name': columns,\n",
    "    'coef': modelCV.coef_, \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-26T19:56:12.594193Z",
     "iopub.status.busy": "2022-04-26T19:56:12.593979Z",
     "iopub.status.idle": "2022-04-26T19:56:12.597523Z",
     "shell.execute_reply": "2022-04-26T19:56:12.596762Z"
    }
   },
   "outputs": [],
   "source": [
    "# Intercept: 6177658.144\n",
    "# Coefficients:\n",
    "#  SqFtTotLiving: 199.27474217544048\n",
    "#  BldgGrade: 137181.13724627026\n",
    "#  YrBuilt: -3564.934870415041\n",
    "#  Bedrooms: -51974.76845567939\n",
    "#  Bathrooms: 42403.059999677665\n",
    "#  PropertyType_Townhouse: 84378.9333363999\n",
    "#  SqFtFinBasement: 7.032178917565108\n",
    "#  PropertyType_Single Family: 22854.87954019308"
   ]
  }
 ],
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